METHOD AND SYSTEM FOR PREDICTING CHILDHOOD OBESITY

Description

RELATED APPLICATIONS

This application claims the benefit of priority under 35 USC 119(e) of U.S. Provisional Patent Application No. 62/882,623 filed on Aug. 5, 2019, the contents of which are all incorporated by reference as if fully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to medicine and, more particularly, but not exclusively, to a method and system for predicting childhood obesity.

Over the past decades, the prevalence of childhood obesity has rapidly increased worldwide. A global analysis demonstrated that in 2016, 50 million girls and 74 million boys worldwide were obese, making it a global public health crisis. Obese children are very likely to have obesity persist into adulthood. Childhood obesity is associated with elevated blood pressure and lipids, and increased risk of diseases, such as asthma, type 2 diabetes, arthritis, and cardiovascular diseases at a later stage of life. Furthermore, childhood obesity can have a negative psycho-social effect.

Preventing excess weight gain in children is important for numerous reasons. Pediatric obesity is a multisystem disease that can greatly impact a child's physical and mental health. It is associated with a greater risk for premature mortality and earlier onset of chronic disorders such as hypertension, dyslipidemia, ischemic heart disease and type 2 diabetes, with insulin resistance identified in obese children as young as 5 years of age. Furthermore, there is currently an underestimation of obesity by parents and physicians and there is currently little guidance for health care professionals to identify infants at risk. Additionally, young age is a suitable time period for intervention, as it is associated with more beneficial long-term outcomes after lifestyle modifications.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: obtaining a plurality of parameters, wherein at least a few of the parameters characterize an infant or toddler subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the plurality of parameters; and receiving from the procedure an output indicative of a likelihood that the infant or toddler subject is expected to develop childhood obesity, wherein the output is related non-linearly to the parameters.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from an electronic health record associated with the infant or toddler subject.

According to some embodiments of the invention the method comprises presenting to a user, by a user interface, a questionnaire and a set of questionnaire controls, receiving a set of response parameters entered by the user using the questionnaire controls, wherein the plurality of parameters comprises the response parameters.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a body liquid test applied to the infant or toddler subject.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter characterizing a parent or a sibling of the infant or toddler subject.

According to some embodiments of the invention the at least one parameter characterizing the parent comprises a parameter extracted from a body liquid test applied to the parent or sibling.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a diagnosis previously recorded for the subject.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter indicative of a pharmaceutical prescribed for the infant or toddler subject.

According to some embodiments of the invention the infant or toddler subject is less than two years of age.

According to some embodiments of the invention the infant or toddler subject is not obese. According to some embodiments of the invention the method wherein the infant or toddler subject has a normal weight. According to some embodiments of the invention the plurality of parameters comprises a weight-for-length score of the infant or toddler subject.

According to some embodiments of the invention the plurality of parameters comprise a weight of the infant or toddler subject at age of from about 4 to about 6 months, a weight of the infant or toddler subject at age of from about 12 to about 16 months, and a weight of the infant or toddler subject at age of from about 18 to about 22 months.

According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a sibling of the infant or toddler subject.

According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a father of the infant or toddler subject.

According to some embodiments of the invention the plurality of parameters comprises a result of a hemoglobin concentration test applied to the infant or toddler subject.

According to some embodiments of the invention the wherein the plurality of parameters comprises a result of a mean platelet volume test applied to the infant or toddler subject.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters listed in Table 1.1.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.1.

According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.1.

According to some embodiments of the invention the plurality of parameters comprises least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of

Table 1.1.

According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: obtaining a plurality of parameters characterizing at least one of a parent and a sibling of an unborn subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the plurality of parameters; and receiving from the procedure an output indicative of a likelihood that the unborn subject is expected to develop childhood obesity after birth, wherein the output is related non-linearly to the parameters.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from an electronic health record associated with the at least one of the parent and the sibling.

According to some embodiments of the invention the method comprises presenting to a user, by a user interface, a questionnaire and a set of questionnaire controls, receiving a set of response parameters entered by the user using the questionnaire controls, wherein the plurality of parameters comprises the response parameters.

According to some embodiments of the invention the plurality of parameters comprises at least one parameter extracted from a body liquid test applied to the at least one of the parent and the sibling.

According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of the sibling.

According to some embodiments of the invention the plurality of parameters comprises a parameter pertaining to a body-mass-index of a father of the unborn subject.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or at least 1,000 or at least 1,500 or more of the parameters listed in Table 1.2.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.2.

According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.2.

According to some embodiments of the invention the plurality of parameters comprises least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of Table 1.2.

According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: presenting on a user interface a questionnaire and a set of questionnaire controls, and receiving from the user interface a set of response parameters entered using the questionnaire controls, wherein the set of response parameters characterizes an infant or toddler subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the set of parameters; and receiving from the procedure an output indicative of a likelihood that the infant or toddler subject is expected to develop childhood obesity, wherein the output is related non-linearly to the parameters.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or more of the parameters listed in Table 1.3.

According to some embodiments of the invention the plurality of parameters comprises at least 10 or at least 12 or at least 14 or at least 16 of the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.3.

According to some embodiments of the invention the plurality of parameters comprises at least 20 or at least 22 or at least 24 or at least 26 of the parameters that are listed at lines 1-50 more preferably lines 1-40 more preferably lines 1-30 of Table 1.3.

According to an aspect of some embodiments of the present invention there is provided a method of predicting likelihood for childhood obesity. The method comprises: presenting on a user interface a questionnaire and a set of questionnaire controls, and receiving from the user interface a set of response parameters entered using the questionnaire controls, wherein the set of response parameters characterizes at least one of a parent and a sibling of an unborn subject; accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for childhood obesity; feeding the procedure with the set of parameters; and receiving from the procedure an output indicative of a likelihood that the unborn subject is expected to develop childhood obesity after birth, wherein the output is related non-linearly to the parameters.

According to some embodiments of the invention the plurality of parameters comprises at least 5 or at least 10 or at least 15 or more of the parameters listed in Table 1.4.

According to some embodiments of the invention the plurality of parameters comprises at least 5 or at least 10 of the parameters that are listed at lines 1-15 of Table 1.4.

Unless otherwise defined, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and/or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

Implementation of the method and/or system of embodiments of the invention can involve performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of embodiments of the method and/or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware or by a combination thereof using an operating system.

For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of method and/or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and/or data and/or a non-volatile storage, for example, a magnetic hard-disk and/or removable media, for storing instructions and/or data. Optionally, a network connection is provided as well. A display and/or a user input device such as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart diagram of a method suitable for predicting likelihood for childhood obesity, according to various exemplary embodiments of the present invention.

FIG. 2 is a schematic illustration of a client-server configuration which can be used according to some embodiments of the present invention for predicting likelihood for childhood obesity, according to some embodiments of the present invention.

FIG. 3 is a diagram illustrating a dataset of nationwide health records used in a study directed to a prediction of childhood obesity and an analysis of risk, according to some embodiments of the present invention.

FIGS. 4A-D show BMI dynamics in early childhood, as obtained in experiments performed according to some embodiments of the present invention. FIG. 4A shows mean BMI z-score for children who were obese (upper curve) versus non obese (lower curve) at 13 years of age. FIG. 4B shows mean change in annual BMI-scores for the same groups of children. Shaded areas are 95% bootstrapped confidence intervals. FIG. 4C shows obesity status transition of the study cohort. Left side: distribution of obesity status at the last available routine checkup before 2 years of age. Right side: distribution of obesity status at 5-6 years of age. Transitions from different obesity states between these two time points are presented. FIG. 4D shows distribution of obesity status at infancy for obese 5-6 years old children.

FIGS. 5A-D show evaluation of obesity prediction model constructed in accordance with some embodiments of the present invention. FIG. 5A shows ROC curve of the model of the present embodiments (solid line) and a baseline model based on the last available routine checkup measurement (dashed). The dots and percentages represent different decision probability thresholds. FIG. 5B is calibration curve. The dots represents deciles of predicted probabilities. The dotted diagonal line represents an ideal calibration. The histogram at the bottom represents predicted probabilities of normal-weight children and obese children. FIG. 5C shows a Precision-Recall curve. The Baseline model is marked with an X. Threshold percentiles are marked on the curves. FIG. 5D shows decision curve analysis containing different treatment strategies of the model according to some embodiments of the present invention (solid curve) and the baseline model (dashed curve). Strategies of treating all (dashed line), treating none (dotted line) and the perfect hypothetical predictor (dot-dash line) are also presented. Abbreviations: auPR/auROC—Area under the PR/ROC curve, PPV—positive predictive value, PR—Precision-Recall, ROC—Receiver-Operator-Characteristic

FIGS. 6A-C show discrimination performances of the obesity prediction model in accordance with some embodiments of the present invention. The discrimination performances are represented by Precision-Recall (auPR) according to last measured WFL percentile (FIG. 6A), different subpopulations of children (FIG. 6B), and the child's age (0-24 months) (FIG. 6C). Abbreviations: auPR—Area under the PR curve, PR—Precision-Recall, WFL—weight for length.

FIGS. 7A-H show interpretation of the model of the present embodiments. FIG. 7A shows Shapley values of different groups of features. FIGS. 7B-H are plots showing in the lower part a histogram of the distribution of a feature in the data and in the upper part a dependence plot of the predicted relative risk for obesity at 5-6 years of age versus the value of the feature for child last WFL z-score (FIG. 7B), child birth weight (FIG. 7C), siblings mean BMI z-score (FIG. 7D), maternal and paternal mean BMI (FIG. 7E); maternal 50 g GCT results during pregnancy (FIG. 7F), duration of antibiotic therapy calculated by the summation of the days in which the child was issued an antibiotics treatment (FIG. 7G), and Child North African Ethnicity index (FIG. 7H). Abbreviations: GCT—glucose challenge test, WFL—Weight-for-Length, y/o—years old.

FIGS. 8A and 8B show results of applying the childhood obesity prediction model of the present embodiments prior to 2 years of age. FIG. 8A shows auPR curve for prediction models of obesity at 5-6 years of age based on features that were extracted up to a predefined endpoint age, ranging from pre-birth to 2 years of age of note, auPR of the prediction model pre-birth and at birth overlap. The baseline model was defined as last routine checkup WFL z-score. FIG. 8B shows relative importance of groups of features for the prediction models, calculated by normalizing to the sum of mean absolute SHAP values for each model. “Others” sums up non-anthropometric or demographic features such as laboratory tests and drug features. Abbreviations: auPR—Area under the PR curve, PR—Precision-Recall, WFL—weight for length

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to medicine and, more particularly, but not exclusively, to a method and system for predicting childhood obesity.

Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

FIG. 1 is a flowchart diagram of a method suitable for predicting likelihood for childhood obesity, according to various exemplary embodiments of the present invention. It is to be understood that, unless otherwise defined, the operations described hereinbelow can be executed either contemporaneously or sequentially in many combinations or orders of execution. Specifically, the ordering of the flowchart diagrams is not to be considered as limiting. For example, two or more operations, appearing in the following description or in the flowchart diagrams in a particular order, can be executed in a different order (e.g., a reverse order) or substantially contemporaneously. Additionally, several operations described below are optional and may not be executed.

The processing operations of the present embodiments can be embodied in many forms. For example, they can be embodied in on a tangible medium such as a computer for performing the operations. They can be embodied on a computer readable medium, comprising computer readable instructions for carrying out the method operations. They can also be embodied in electronic device having digital computer capabilities arranged to run the computer program on the tangible medium or execute the instruction on a computer readable medium.

Computer programs implementing the method according to some embodiments of this invention can commonly be distributed to users on a distribution medium such as, but not limited to, CD-ROM, flash memory devices, flash drives, or, in some embodiments, drives accessible by means of network communication, over the internet (e.g., within a cloud environment), or over a cellular network. From the distribution medium, the computer programs can be copied to a hard disk or a similar intermediate storage medium. The computer programs can be run by loading the computer instructions either from their distribution medium or their intermediate storage medium into the execution memory of the computer, configuring the computer to act in accordance with the method of this invention. Computer programs implementing the method according to some embodiments of this invention can also be executed by one or more data processors that belong to a cloud computing environment. All these operations are well-known to those skilled in the art of computer systems. Data used and/or provided by the method of the present embodiments can be transmitted by means of network communication, over the internet, over a cellular network or over any type of network, suitable for data transmission.

The method according to preferred embodiments of the present invention can be embedded into healthcare systems and may allow identification and implementation of prevention strategies for children at high risk for obesity.

The method begins at 10 and continues to 11 at which a plurality of parameters characterizing is obtained. The inventors discovered that the likelihood for childhood obesity can be predicted both for infant or toddler subjects and for unborn subjects, e.g., during the pregnancy of a female carrying the unborn subject.

As used herein “infant” refers to an individual not more that 1 year of age, and “toddler” refers to an individual above 1 year of age and not more than 3 years of age”

Thus, in some embodiments of the present invention the method predicts likelihood that an infant or toddler subject is expected to develop childhood obesity, and in some embodiments of the present invention the method predicts unborn subject is expected to develop childhood obesity after birth. When the subject is an infant or toddler subject he or she is preferably of less than two years of age. The method of the present embodiments is typically used for estimating the likelihood that the subject is expected to develop childhood obesity at age greater than the toddler age, e.g., more than 4 years of age, for example, from about 5 to about 6 years of age.

When the subject is an infant or toddler subject, at least one of the parameters that are obtained at 11, more preferably more than one of these parameters, more preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters are extracted from an electronic health record associated with the subject. Parameters extracted from an electronic health record can include, but are not limited to, anthropometric parameters (e.g., height, weight, body mass index, weight-for-length score), blood pressure measurements, blood and urine laboratory tests, diagnoses recorded by physicians, and/or pharmaceuticals prescribed to the subject.

In some embodiments of the present invention at least one of the parameters that are obtained at 11, more preferably more than one of these parameters, more preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more of the parameters are extracted from an electronic health record associated with a parent (mother and/or father) and/or a sibling (brother and or sister) of the subject. These parameters can include any of the aforementioned parameters associated with the subject, except that they describe the respective parent or sibling (e.g., anthropometric parameters, blood pressure measurements, blood and urine laboratory tests, diagnoses, pharmaceuticals).

When the subject is an unborn subject, there are typically no parameters that describe the subject itself, and so the parameters that are obtained at 11 are typically associated with a parent (mother and/or father) and/or a sibling (brother and or sister) of the subject, as further detailed hereinabove.

A list of parameters from which the parameters can be selected when the subject is an infant or toddler subject is provided in Table 1.1 of the Examples section that follows, and list of parameters from which the parameters can be selected when the subject is an unborn subject is provided in Table 1.2 of the Examples section that follows. In some embodiments of the present invention at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 are selected from the parameters listed in Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). Preferably, but not necessarily, at least 10 or at least 12 or at least 14 or at least 16 of the parameters are selected from the parameters that are listed at lines 1-40 more preferably lines 1-30 more preferably lines 1-20 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). In some embodiments, at least 20 or at least 22 or at least 24 or at least 26 or at least 28 or at least 30 or at least 32 or at least 34 or at least 36 of the parameters are selected from the parameters that are listed at lines 1-50 more preferably lines 1-45 more preferably lines 1-40 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject). In some embodiments, at least 50 or at least 60 or at least 70 or at least 80 or at least 90 of the parameters are selected from the parameters that are listed at lines 1-300 more preferably lines 1-200 more preferably lines 1-100 of Table 1.1 (for an infant or toddler subject) or Table 1.2 (for an unborn subject).

Also contemplated are embodiments in which the parameters are selected from a set of response parameters that are provided by a person on behalf of the subject (e.g., a parent, a sibling, etc.), by responding to a questionnaire presented to the person. These parameters can include anthropometric parameters (e.g., height, weight, body mass index, weight-for-length score), one or more parameters indicative of the age of the subject (if born), and one or more parameters indicative of the ethnicity of the subject. A list of parameters which can be provided by responding to the questionnaire is provided in Table 1.3 for the case in which the subject is an infant or toddler subject, and in Table 1.4 for the case in which the subject is an unborn subject.

In some embodiments of the present invention the parameters include only parameters extracted from one or more electronic health records, in some embodiments of the present invention the parameters include only response parameters that are provided on behalf of the subject, and in some embodiments of the present invention the parameters include both parameters extracted from electronic health record(s) and response parameters that are provided by the subject or on her behalf.

In some embodiments of the present invention the electronic health record(s) include a record that is associated with the subject, in some embodiments of the present invention parameters the electronic health record(s) include records that are associated with at least one of a parent and a sibling of the subject, and in some embodiments of the present invention the electronic health record(s) include at least one record that is associated with the subject, and at least one record that is associated with a parent and/or a sibling of the subject.

The number of parameters that are extracted from the electronic health record(s) associated is preferably at least 10 or at least 20 or at least 30 or at least 40 or at least 50 or at least 100 or at least 200 or at least 300 or at least 400 or at least 500 or more. The number of response parameters that are provided by the subject or on her behalf is preferably 100 or less, or 80 or less, or 70 or less. The advantage of this embodiment is that a relative small number of parameter allows the subject to manually respond to the questionnaire at a relatively short time.

When the parameters include both parameters extracted from electronic health record(s), and response parameters that are provided on behalf of the subject, the number of parameters that are extracted from the electronic health record(s) is optionally and preferably significantly larger (e.g., at least 2 or at least 3 or at least 4 or at least 5 or at least 6 or at least 7 or at least 8 or at least 9 or at least 10 times larger) than the number of response parameters that are provided on behalf of the subject.

In some embodiments of the present invention at least one of the parameters is extracted from a body liquid test applied to the infant or toddler subject. Representative examples of body liquid tests from which a parameter can extracted from a body liquid test applied to the infant or toddler subject according to some embodiments of the present invention include, without limitation, Albumin test, Alk. phosphatase test, Atypical lymph. %-dif test, Atypical lymph-dif test, Basophils percentage (Baso %) test, Basophils (Baso abs) test, Bilirubin total test, Bilirubin-direct test, Calcium test, Chloride test, Cholesterol test, C-reactive protein test, Creatinine test, Eos % test, Eos.abs test, Eosinophils abs-dif test, Eosinophils %-dif test, Ferritin test, Gamma glutamyl transferase (Ggt) test, Glucose test, Got (ast) test, Alanine aminotransferase (Gpt (alt)) test, hemoglobin concentration (Hb) test, Hematocrit (Hct) test, Hematocrit/hemoglobin (Hct/hgb) ratio test, Hyper % test, Hypochromic red cells (Hypo %) test, Iron test, Ldh test, Luc abs test, Luc % test, Lym % test, Lymp.abs test, Lymphocytes %-dif test, Lymphocytes abs-dif test, Macro % test, Mean cell hemoglobin (Mch) test, mean hemoglobin concentration (Mchc) test, mean corpuscular volume (Mcv) test, Micro % test, Micro %/hypo % test, Mono % test, Mono.abs test, Monocytes abs-dif test, Monocytes %-dif test, mean platelet volume (Mpv) test, Mpxi test, Neut % test, Neut.abs test, Neutrophils abs-dif test, Neutrophils %-dif test, Pct test, Pdw test, Phosphorus test, platelet count blood (Plt) test, Potassium test, Protein-total test, Rbc test, red cell distribution width (Rdw) test, Red blood cell distribution width presented as the coefficient of variation (Rdw-cv) test, Sodium test, Stabs %-dif test, Stabs abs-dif test, T4-free test, Transferrin test, Triglycerides test, Thyroid-stimulating hormone (Tsh) test, Urea test, Uric acid test, and white blood cells (Wbc) test.

In some embodiments of the present invention at least one of the parameters is extracted from a body liquid test applied to the mother of the infant or toddler subject during pregnancy of the mother with the infant or toddler subject. Representative examples of body liquid tests from which a parameter can extracted from a body liquid test applied to the mother according to some embodiments of the present invention include, without limitation, Albumin, Alk. phosphatase, Alpha fetoprotein tm, Amylase, Aptt-r, Aptt-sec, Baso %, Baso abs, Bilirubin indirect, Bilirubin total, Bilirubin-direct, Blood type, Calcium, Chloride, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Ck-creat.kinase(cpk), Cmv igg, Control ptt, Creatinine, Dhea sulphate, Eos %, Eos.abs, Eosinophils abs-dif, Eosinophils %-dif, Esr, Estradiol (e-2), Ferritin, Fibrinogen calcu, Fibrinogen, Folic acid, Fsh, Ggt, Globulin, Glom.filtr.rate, Glucose (gtt) 0′, Glucose (gtt) 120′, Glucose (gtt) 180′, Glucose (gtt) 60′, Glucose 50 g, Glucose, Got (ast), Gpt (alt), Hb, Hba, Hba2, Hbf, Hct, Hct/hgb ratio, Hdw, Hemoglobin a, Hemoglobin alc %, Hepatitis bs ab, Hyper %, Hypo %, Iron, Ldh, Lh, Li, Luc abs, Luc %, Lym %, Lymp.abs, Lymphocytes %-dif, Lymphocytes abs-dif, Macro %, Magnesium, Mch, Mchc, Mcv, Micro %, Micro %/hypo %, Mono %, Mono.abs, Monocytes abs-dif, Monocytes %-dif, Mpv, Mpxi, Neut %, Neut.abs, Neutrophils abs-dif, Neutrophils %-dif, Non-hdl_cholesterol, Normoblast. %, Normoblast.abs, Pct, Pdw, Phosphorus, Plt, Potassium, Progesterone, Prolactin, Protein-total, Pt %, Pt-inr, Pt-sec, Rbc, Rdw, Rdw-cv, Rubella ab igg, Sodium, Stabs %-dif, Stabs abs-dif, T3-free, T4-free, Toxoplasma igg, Transferrin, Triglycerides, Tsh, Urea, Uric acid, Vitamin b12, Vitamin d (25-oh), and Wbc.

In some embodiments of the present invention at least one of the parameters is extracted from a test applied to the mother of the infant or toddler subject prior to the pregnancy of the mother with the infant or toddler subject. Representative examples such tests include, without limitation, 17-oh-progesterone, Albumin, Alk. phosphatase, Aly, Aly %, Amylase, Androstenedione, Anti cardiolipin igg, Anti cardiolipin igm, Antithrombin-iii, Aptt-r, Aptt-sec, Baso %, Baso abs, Bilirubin indirect, Bilirubin total, Bilirubin-direct, Blood type, BMI, Ca-125, Calcium, Chloride, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Ck-creat.kinase(cpk), Cmv igg, Complement c3, Complement c4, Control ptt, Cortisol-blood, C-reactive protein, Creatinine, Dhea sulphate, Eos %, Eos.abs, Eosinophils %-dif, Esr, Estradiol (e-2), Ferritin, Fibrinogen calcu, Fibrinogen, Folic acid, Free androgen index, Fsh, Ggt, Globulin, Glom.filtr.rate, Glucose 50 g, Glucose, Got (ast), Gpt (alt), Hb, Hba2, Hbf, Hct, Hct/hgb ratio, Hdw, Hemoglobin a, Hemoglobin alc %, Hepatitis bs ab, Hyper %, Hypo %, Iga, Iron, Ldh, Lh, Lic, Lic %, Luc abs, Luc %, Lym %, Lymp.abs, Lymphocytes %-dif, Lymphocytes abs-dif, Macro %, Magnesium, Mch, Mchc, Mcv, Micro %, Micro %/hypo %, Mono %, Mono.abs, Monocytes abs-dif, Monocytes %-dif, Mpv, Mpxi, Neut %, Neut.abs, Neutrophils abs-dif, Neutrophils %-dif, Non-hdl_cholesterol, Normoblast. %, Normoblast.abs, Pct, Pdw, Phosphorus, Plt, Potassium, Progesterone, Prolactin, Protein c activity, Protein-total, Prot-s antigen (free, Pt %, Pt-inr, Pt-sec, Rbc, Rdw, Rdw-cv, Rubella ab igg, Shbg, Sodium, T3-free, T3-total, T4-free, Testosterone-total, Toxoplasma igg, Transferrin, Triglycerides, Tsh, Urea, Uric acid, Vitamin b12, Vitamin d (25-oh), Vldl, Wbc, and Weight.

In some embodiments of the present invention the plurality of parameters comprises a result of a blood glucose test applied to the mother of the subject.

In some embodiments of the present invention at least one of the parameters is extracted from a test applied to the father of the infant or toddler subject. Representative examples of such tests include, without limitation, Age at the birth of the subject, BMI count, BMI max, BMI mean, BMI median, BMI min, BMI standard deviation (std), Height count, Height max, Height mean, Height median, Height min, Height std, max Cholesterol-hdl, max Cholesterol, max Cholesterol/hdl, max Cholesterol-ldl calc, max Glucose, max Non-hdl_cholesterol, max Triglycerides, mean Cholesterol-hdl, mean Cholesterol, mean Cholesterol/hdl, mean Cholesterol-ldl calc, mean Glucose, mean Non-hdl_cholesterol, mean Triglycerides, median Cholesterol-hdl, median Cholesterol, median Cholesterol/hdl, median Cholesterol-ldl calc, median Glucose, median Non-hdl_cholesterol, median Triglycerides, min Cholesterol-hdl, min Cholesterol, min Cholesterol/hdl, min Cholesterol-ldl calc, min Glucose, min Non-hdl_cholesterol, min Triglycerides, std Cholesterol-hdl, std Cholesterol, std Cholesterol/hdl, std Cholesterol-ldl calc, std Glucose, std Non-hdl_cholesterol, std Triglycerides, Weight count, Weight max, Weight mean, Weight median, Weight min, and Weight std.

In some embodiments of the present invention one or more of the parameters is a result of a hemoglobin concentration test (Hb) applied to the subject.

In some embodiments of the present invention one or more of the parameters is a result of a mean platelet volume test (Mpv) applied to the subject.

In some embodiments of the present invention one or more of the parameters is a result of a Basophils percentage test (Baso %) applied to the subject.

In some embodiments of the present invention one or more of the parameters is a result of a red cell distribution width test (Rdw) applied to the subject.

In some embodiments of the present invention one or more of the parameters is a result of a platelet count blood test (plt) applied to the subject.

In some embodiments of the present invention the parameters comprise at least one parameter extracted from a clinical or hospital diagnosis previously recorded for the subject. Representative examples of clinical and hospital diagnoses which can be used as parameters according to some embodiments of the present invention include, without limitation, Abdominal pain, Abnormal loss of weight, Abnormal weight gain, Accident/injury; nos, Acquired deformities of other parts of limbs, Acute and unspecified inflammation of lacrimal passages, Acute bronchiolitis, Acute bronchitis, Acute conjunctivitis, Acute laryngitis, Acute laryngotracheitis, Acute lymphadenitis, Acute myringitis without mention of otitis media, Acute nasopharyngitis (common cold), Acute nonsuppurative otitis media, Acute pharyngitis, Acute suppurative otitis media, Acute tonsillitis, Acute upper respiratory infections of multiple or unsp.sites, Acute upper respiratory infections of unspecified site, Agranulocytosis, Allergic rhinitis, Allergy, unspecified, not elsewhere classified, Allergy/allergic react nos, Anal fissure, Anemia other/unspecified, Anorexia, Asthma, Asthma, unspecified, Atopic dermatitis/eczema, Benign neoplasm of skin, site unspecified, Blepharitis, Blisters with epidermal loss,burn 2nd.deg.unspecified site, Bronchopneumonia, organism unspecified, Candidiasis of mouth, Candidiasis of skin and nails, Candidiasis of unspecified site, Cellulitis and abscess of finger, Cellulitis and abscess of unspecified sites, Chronic rhinitis, Chronic serous otitis media, Colitis, enteritis, gastroenteritis presumed infectious origin, Congenital anomalies of lower limb, including pelvic girdle, Congenital dislocation of hip, Congenital musculoskeletal deformities of sternocleidomastoid, Constipation, Contact dermatitis and other eczema, Contact dermatitis and other eczema, unspecified cause, Contusion of unspecified site, Convulsions, Cough, Croup, Delivery in a completely normal case, Dermatitis due to food taken internally, Dermatophytosis of the body, Diaper or napkin rash, Diarrhea, Diseases and other conditions of the tongue, Disorders relating to other preterm infants, Dyspnea and respiratory abnormalities, Enlargement of lymph nodes, Enteritis due to specified virus, Enterobiasis, Esophagitis, Feeding difficulties and mismanagement, Fever, Gastrointestinal hemorrhage, Hand, foot, and mouth disease, Hearing complaints, Hearing loss, Hemangioma of unspecified site, Herpangina, Hip symptoms/complaints, Hydrocele, Hydronephrosis, Hypermetropia, Hypertrophy of tonsils and adenoids, Impetigo, Infectious colitis, enteritis, and gastroenteritis, Infectious diarrhea, Infectious mononucleosis, Infective otitis externa, Influenza, Inguinal hernia, without mention of obstruction or gangrene, Injuries, Insect bite, Insect bite, nonvenomous face, neck, scalp without infection, Intestinal malabsorption, Iron deficiency anemia, unspecified, Irritable infant, Jaundice, unspecified, not of newborn, Laceration/cut, Lack of coordination, Lack of expected normal physiological development, Late effect of injury to cranial nerve, Laxity of ligament, Nausea and vomiting, Nervousness, Nonsuppurative otitis media, not specified as acute or chronic, Open wound of face, without mention of complication, Oral aphthae, Otalgia, Other and unspec.noninfectious gastroenteritis and colitis, Other and unspecified chronic nonsuppurative otitis media, Other and unspecified injury to unspecified site, Other atopic dermatitis and related conditions, Other diseases of conjunctiva due to viruses and chlamydiae, Other diseases of nasal cavity and sinuses, Other serum reaction, not elsewhere classified, Other specified disease of white blood cells, Other specified erythematous conditions, Other specified viral exanthemata, Other speech disturbance, Other symptoms involving digestive system, Other viral diseases; nos, Otorrhea, Pneumonia, Pneumonia, organism unspecified, Posttraumatic wound infection not elsewhere classified, Premat/immature liveborn infant, Rash and other nonspecific skin eruption, Seborrhea, Seborrheic dermatitis, unspecified, Serous otitis media;glue, Sleep disturbances, Sneezing/nasal congestion, Stenosis and insufficiency of lacrimal passages, Stomatitis, Strabismus and other disorders of binocular eye movements, Stridor, Teething syndrome, Tongue tie, Torticollis, unspecified, U.r.i. (head cold), Umbilical hernia without mention of obstruction or gangrene, Undescended testicle, Unsp.adv.effect of drug,medicinal/biological substance n.e.s., Unsp.viral infect.in conditions classif.elsewhere, unsp.site, Unspecified fetal and neonatal jaundice, Unspecified otitis media, Urinary tract infection, site not specified, Urticaria, Varicella without mention of complication, Viral exanthem, unspecified, Viral pneumonia, Volume depletion disorder, Vomiting (excl.preg. w06), and Wheezing baby syndrome.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Aciclovir, Ahiston drop cd, Amoxicillin, Azithromycin, Bethamethasone, Budesonide, Cefaclor, Cefalexin, Ceftriaxone, Cefuroxime, Co-amoxiclav cd, Co-trimoxazole cd, Desloratadine, Dimethindene, Erythromycin, Fluticasone, Ipratropium bromide, Ketotifen, Loratadine, Mebendazole, Metronidazole, Montelukast, Phenoxymethylpenicillin, Prednisolone, Prothiazine/promethazine expectorant cd, Ranitidine, Salbutamol, and Terbutaline.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Salbutamol prescriptions provided for the infant or toddler subject.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Bethamethasone prescriptions provided for the infant or toddler subject.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a count of Budesonide prescriptions provided for the infant or toddler subject.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the mother of the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Aciclovir, Amoxicillin, Anti-d (rh) immunoglobulin, Aspirin, Bethamethasone, Budesonide, Cabergoline, Carbamazepine, Cefalexin, Cefuroxime, Cetirizine, Choriogonadotropin alfa, Chorionic gonadotrophin, Ciprofloxacin, Citalopram, Clarithromycin, Clomifene, Clonazepam, Co-amoxiclav cd, Colchicine, Desloratadine, Desogestrel and ethinylestradiol, Desogestrel, Dexamethasone, Doxycycline, Drospirenone and ethinylestradiol, Dydrogesterone, Enoxaparin, Escitalopram, Estradiol, Famotidine, Fexofenadine, Fluconazole, Fluoxetine, Fluticasone, Follitropin alfa, Follitropin beta, Gestodene and ethinylestradiol, Human menopausal gonadotrophin, Ipratropium bromide, Lamotrigine, Lansoprazole, Levothyroxine sodium, Loratadine, Mebendazole, Medroxyprogesterone, Methylphenidate, Metronidazole, Nitrofurantoin, Norethisterone, Norgestimate and ethinylestradiol, Ofloxacin, Omeprazole, Paroxetine, Phenoxymethylpenicillin, Prednisone, Progesterone, Progyluton cd, Roxithromycin, Salbutamol, Seretide cd, Sertraline, Simvastatin, Symbicort/duoresp, and Triptorelin.

In some embodiments of the present invention the parameters comprise at least one parameter indicative of a pharmaceutical prescribed for the father of the subject. Representative examples of prescribed pharmaceuticals which can be used as parameters according to some embodiments of the present invention include, without limitation, Amlodipine, Atenolol, Atorvastatin, Bezafibrate, Bisoprolol, Cholesterol-hdl, Cholesterol, Cholesterol/hdl, Cholesterol-ldl calc, Enalapril, Glucose, Insulin glargine, Metformin and sitagliptin cd, Metformin, Nifedipine, Nifedipine-cd, Non-hdl_cholesterol, Pravastatin, Propranolol, Ramipril, Ramipril-hydrochlorothiazide cd, Rosuvastatin, Simvastatin, and Triglycerides.

In some embodiments of the present invention the parameters comprise at least one parameter extracted from a clinical or hospital diagnosis previously recorded for the father of subject. Representative examples of clinical and hospital diagnoses which can be used as parameters according to some embodiments of the present invention include, without limitation, Diabetes mellitus, unspecified Obesity, Obesity (BMI>30), other and unspecified hyperlipidemia, Essential hypertension, Morbid obesity, unspecified essential hypertension, Overweight (BMI<30), other abnormal glucose, Lipid metabolism disorder, Impaired fasting glucose, Disorders of lipoid metabolism, Diabetes mellitus without mention of complication, and Adult-onset type diabetes mellitus without complication.

Referring again to FIG. 1, the method proceeds to 12 at which a computer readable medium storing a machine learning procedure is accessed. The machine learning procedure is trained for predicting likelihoods for childhood obesity.

As used herein the term “machine learning” refers to a procedure embodied as a computer program configured to induce patterns, regularities, or rules from previously collected data to develop an appropriate response to future data, or describe the data in some meaningful way.

Representative examples of machine learning procedures suitable for the present embodiments, include, without limitation, clustering, association rule algorithms, feature evaluation algorithms, subset selection algorithms, support vector machines, classification rules, cost-sensitive classifiers, vote algorithms, stacking algorithms, Bayesian networks, decision trees, neural networks, instance-based algorithms, linear modeling algorithms, k-nearest neighbors (KNN) analysis, ensemble learning algorithms, probabilistic models, graphical models, logistic regression methods (including multinomial logistic regression methods), gradient ascent methods, singular value decomposition methods and principle component analysis.

Following is an overview of some machine learning procedures suitable for the present embodiments.

Support vector machines are algorithms that are based on statistical learning theory. A support vector machine (SVM) according to some embodiments of the present invention can be used for classification purposes and/or for numeric prediction. A support vector machine for classification is referred to herein as “support vector classifier,” support vector machine for numeric prediction is referred to herein as “support vector regression”.

An SVM is typically characterized by a kernel function, the selection of which determines whether the resulting SVM provides classification, regression or other functions. Through application of the kernel function, the SVM maps input vectors into high dimensional feature space, in which a decision hyper-surface (also known as a separator) can be constructed to provide classification, regression or other decision functions. In the simplest case, the surface is a hyper-plane (also known as linear separator), but more complex separators are also contemplated and can be applied using kernel functions. The data points that define the hyper-surface are referred to as support vectors.

The support vector classifier selects a separator where the distance of the separator from the closest data points is as large as possible, thereby separating feature vector points associated with objects in a given class from feature vector points associated with objects outside the class. For support vector regression, a high-dimensional tube with a radius of acceptable error is constructed which minimizes the error of the data set while also maximizing the flatness of the associated curve or function. In other words, the tube is an envelope around the fit curve, defined by a collection of data points nearest the curve or surface.

An advantage of a support vector machine is that once the support vectors have been identified, the remaining observations can be removed from the calculations, thus greatly reducing the computational complexity of the problem. An SVM typically operates in two phases: a training phase and a testing phase. During the training phase, a set of support vectors is generated for use in executing the decision rule. During the testing phase, decisions are made using the decision rule. A support vector algorithm is a method for training an SVM. By execution of the algorithm, a training set of parameters is generated, including the support vectors that characterize the SVM. A representative example of a support vector algorithm suitable for the present embodiments includes, without limitation, sequential minimal optimization.

In KNN analysis, the affinity or closeness of objects is determined. The affinity is also known as distance in a feature space between objects. Based on the determined distances, the objects are clustered and an outlier is detected. Thus, the KNN analysis is a technique to find distance-based outliers based on the distance of an object from its kth-nearest neighbors in the feature space. Specifically, each object is ranked on the basis of its distance to its kth-nearest neighbors. The farthest away object is declared the outlier. In some cases the farthest objects are declared outliers. That is, an object is an outlier with respect to parameters, such as, a k number of neighbors and a specified distance, if no more than k objects are at the specified distance or less from the object. The KNN analysis is a classification technique that uses supervised learning. An item is presented and compared to a training set with two or more classes. The item is assigned to the class that is most common amongst its k-nearest neighbors. That is, compute the distance to all the items in the training set to find the k nearest, and extract the majority class from the k and assign to item.

Association rule algorithm is a technique for extracting meaningful association patterns among features.

The term “association”, in the context of machine learning, refers to any interrelation among features, not just ones that predict a particular class or numeric value. Association includes, but it is not limited to, finding association rules, finding patterns, performing feature evaluation, performing feature subset selection, developing predictive models, and understanding interactions between features.

The term “association rules” refers to elements that co-occur frequently within the datasets. It includes, but is not limited to association patterns, discriminative patterns, frequent patterns, closed patterns, and colossal patterns.

A usual primary step of association rule algorithm is to find a set of items or features that are most frequent among all the observations. Once the list is obtained, rules can be extracted from them.

The aforementioned self-organizing map is an unsupervised learning technique often used for visualization and analysis of high-dimensional data. Typical applications are focused on the visualization of the central dependencies within the data on the map. The map generated by the algorithm can be used to speed up the identification of association rules by other algorithms. The algorithm typically includes a grid of processing units, referred to as “neurons”. Each neuron is associated with a feature vector referred to as observation. The map attempts to represent all the available observations with optimal accuracy using a restricted set of models. At the same time the models become ordered on the grid so that similar models are close to each other and dissimilar models far from each other. This procedure enables the identification as well as the visualization of dependencies or associations between the features in the data.

Feature evaluation algorithms are directed to the ranking of features or to the ranking followed by the selection of features based on their impact.

Information gain is one of the machine learning methods suitable for feature evaluation. The definition of information gain requires the definition of entropy, which is a measure of impurity in a collection of training instances. The reduction in entropy of the target feature that occurs by knowing the values of a certain feature is called information gain. Information gain may be used as a parameter to determine the effectiveness of a feature in explaining the response to the treatment. Symmetrical uncertainty is an algorithm that can be used by a feature selection algorithm, according to some embodiments of the present invention. Symmetrical uncertainty compensates for information gain's bias towards features with more values by normalizing features to a [0,1] range.

Subset selection algorithms rely on a combination of an evaluation algorithm and a search algorithm. Similarly to feature evaluation algorithms, subset selection algorithms rank subsets of features. Unlike feature evaluation algorithms, however, a subset selection algorithm suitable for the present embodiments aims at selecting the subset of features with the highest impact on predicting likelihood for childhood obesity, while accounting for the degree of redundancy between the features included in the subset. The benefits from feature subset selection include facilitating data visualization and understanding, reducing measurement and storage requirements, reducing training and utilization times, and eliminating distracting features to improve classification.

Two basic approaches to subset selection algorithms are the process of adding features to a working subset (forward selection) and deleting from the current subset of features (backward elimination). In machine learning, forward selection is done differently than the statistical procedure with the same name. The feature to be added to the current subset in machine learning is found by evaluating the performance of the current subset augmented by one new feature using cross-validation. In forward selection, subsets are built up by adding each remaining feature in turn to the current subset while evaluating the expected performance of each new subset using cross-validation. The feature that leads to the best performance when added to the current subset is retained and the process continues. The search ends when none of the remaining available features improves the predictive ability of the current subset. This process finds a local optimum set of features.

Backward elimination is implemented in a similar fashion. With backward elimination, the search ends when further reduction in the feature set does not improve the predictive ability of the subset. The present embodiments contemplate search algorithms that search forward, backward or in both directions. Representative examples of search algorithms suitable for the present embodiments include, without limitation, exhaustive search, greedy hill-climbing, random perturbations of subsets, wrapper algorithms, probabilistic race search, schemata search, rank race search, and Bayesian classifier.

A decision tree is a decision support algorithm that forms a logical pathway of steps involved in considering the input to make a decision.

The term “decision tree” refers to any type of tree-based learning algorithms, including, but not limited to, model trees, classification trees, and regression trees.

A decision tree can be used to classify the datasets or their relation hierarchically. The decision tree has tree structure that includes branch nodes and leaf nodes. Each branch node specifies an attribute (splitting attribute) and a test (splitting test) to be carried out on the value of the splitting attribute, and branches out to other nodes for all possible outcomes of the splitting test. The branch node that is the root of the decision tree is called the root node. Each leaf node can represent a classification (e.g., whether a particular parameter influences on the likelihood for childhood obesity) or a value (e.g., the predicted likelihood for childhood obesity). The leaf nodes can also contain additional information about the represented classification such as a confidence score that measures a confidence level in the represented classification (i.e., the accuracy of the prediction).

Regression techniques which may be used in accordance with some embodiments the present invention include, but are not limited to linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal Probit-Regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression (MLR) and truncated regression.

A logistic regression or logit regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (a dependent variable that can take on a limited number of values, whose magnitudes are not meaningful but whose ordering of magnitudes may or may not be meaningful) based on one or more predictor variables. Logistic regression may also predict the probability of occurrence for each data point. Logistic regressions also include a multinomial variant. The multinomial logistic regression model is a regression model which generalizes logistic regression by allowing more than two discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.). For binary-valued variables, a cutoff between the 0 and 1 associations is typically determined using the Yuden Index.

A Bayesian network is a model that represents variables and conditional interdependencies between variables. In a Bayesian network variables are represented as nodes, and nodes may be connected to one another by one or more links. A link indicates a relationship between two nodes. Nodes typically have corresponding conditional probability tables that are used to determine the probability of a state of a node given the state of other nodes to which the node is connected. In some embodiments, a Bayes optimal classifier algorithm is employed to apply the maximum a posteriori hypothesis to a new record in order to predict the probability of its classification, as well as to calculate the probabilities from each of the other hypotheses obtained from a training set and to use these probabilities as weighting factors for future predictions of the likelihood for childhood obesity. An algorithm suitable for a search for the best Bayesian network, includes, without limitation, global score metric-based algorithm. In an alternative approach to building the network, Markov blanket can be employed. The Markov blanket isolates a node from being affected by any node outside its boundary, which is composed of the node's parents, its children, and the parents of its children.

Instance-based techniques generate a new model for each instance, instead of basing predictions on trees or networks generated (once) from a training set.

The term “instance”, in the context of machine learning, refers to an example from a dataset.

Instance-based techniques typically store the entire dataset in memory and build a model from a set of records similar to those being tested. This similarity can be evaluated, for example, through nearest-neighbor or locally weighted methods, e.g., using Euclidian distances. Once a set of records is selected, the final model may be built using several different techniques, such as the naive Bayes.

Neural networks are a class of algorithms based on a concept of inter-connected “neurons.” In a typical neural network, neurons contain data values, each of which affects the value of a connected neuron according to connections with predefined strengths, and whether the sum of connections to each particular neuron meets a predefined threshold. By determining proper connection strengths and threshold values (a process also referred to as training), a neural network can achieve efficient recognition of images and characters. Oftentimes, these neurons are grouped into layers in order to make connections between groups more obvious and to each computation of values. Each layer of the network may have differing numbers of neurons, and these may or may not be related to particular qualities of the input data.

In one implementation, called a fully-connected neural network, each of the neurons in a particular layer is connected to and provides input value to those in the next layer. These input values are then summed and this sum compared to a bias, or threshold. If the value exceeds the threshold for a particular neuron, that neuron then holds a positive value which can be used as input to neurons in the next layer of neurons. This computation continues through the various layers of the neural network, until it reaches a final layer. At this point, the output of the neural network routine can be read from the values in the final layer. Unlike fully-connected neural networks, convolutional neural networks operate by associating an array of values with each neuron, rather than a single value. The transformation of a neuron value for the subsequent layer is generalized from multiplication to convolution.

The machine learning procedure used according to some embodiments of the present invention is a trained machine learning procedure, which provides output that is related non-linearly to the parameters with which it is fed.

A machine learning procedure can be trained according to some embodiments of the present invention by feeding a machine learning training program with parameters that characterizes each of a cohort of subjects that has been diagnosed as either having or not having childhood obesity at obesity at age greater than the toddler age. Once the data are fed, the machine learning training program generates a trained machine learning procedure which can then be used without the need to re-train it.

For example, when it is desired to employ decision trees, machine learning training program learns the structure of each tree in a plurality of decision trees (e.g., how many nodes there are in each tree, and how these are connected to one another), and also selects the decision rules for split nodes of each tree. At least a portion of the decision rules relate to one or more of the parameters that characterize the subject. A simple decision rule may be a threshold for the value of a particular parameter, but more complex rules, relating to more than one parameter are also contemplated. The machine learning training program also accumulates data at the leaves of the trees. The structures of the trees, the decision rules for the split nodes, and the data at the leaves are all selected by the machine learning training program, automatically and typically without user intervention, such that the parameters at the root of the trees provide the likelihood for childhood obesity at the leaves of the trees. The final result of the machine learning training program in this case is a set of trees, where the structures, the decision rules for split nodes, and leaf data for each trees are defined by the machine learning training program.

The method proceeds to 13 at which the trained machine learning procedure is fed with the parameters, and to 14 at which an output indicative of the likelihood that the subject is expected to develop childhood obesity is received from the procedure. Preferably, the procedure provides the likelihood that the subject is expected to develop childhood obesity at an age greater than the toddler are, as further detailed hereinabove. In some embodiments of the present invention the method proceeds to 15 at which a report predating to the likelihood is generated. The report can be displayed on a display device or transmitted to a computer readable medium.

The method ends at 16.

The prediction of likelihood for childhood obesity can be executed according to some embodiments of the present invention by a server-client configuration, as will now be explained with reference to FIG. 2.

FIG. 2 illustrates a client computer 30 having a hardware processor 32, which typically comprises an input/output (I/O) circuit 34, a hardware central processing unit (CPU) 36 (e.g., a hardware microprocessor), and a hardware memory 38 which typically includes both volatile memory and non-volatile memory. CPU 36 is in communication with I/O circuit 34 and memory 38. Client computer 30 preferably comprises a user interface, e.g., a graphical user interface (GUI), 42 in communication with processor 32. I/O circuit 34 preferably communicates information in appropriately structured form to and from GUI 42. Also shown is a server computer 50 which can similarly include a hardware processor 52, an I/O circuit 54, a hardware CPU 56, a hardware memory 58. I/O circuits 34 and 54 of client 30 and server 50 computers preferable operate as transceivers that communicate information with each other via a wired or wireless communication. For example, client 30 and server 50 computers can communicate via a network 40, such as a local area network (LAN), a wide area network (WAN) or the Internet. Server computer 50 can be in some embodiments be a part of a cloud computing resource of a cloud computing facility in communication with client computer 30 over the network 40.

GUI 42 and processor 32 can be integrated together within the same housing or they can be separate units communicating with each other. GUI 42 can optionally and preferably be part of a system including a dedicated CPU and I/O circuits (not shown) to allow GUI 42 to communicate with processor 32. Processor 32 issues to GUI 42 graphical and textual output generated by CPU 36. Processor 32 also receives from GUI 42 signals pertaining to control commands generated by GUI 42 in response to user input. GUI 42 can be of any type known in the art, such as, but not limited to, a keyboard and a display, a touch screen, and the like. In preferred embodiments, GUI 42 is a GUI of a mobile device such as a smartphone, a tablet, a smartwatch and the like. When GUI 42 is a GUI of a mobile device, the CPU circuit of the mobile device can serve as processor 32 and can execute the method optionally and preferably by executing code instructions.

Client 30 and server 50 computers can further comprise one or more computer-readable storage media 44, 64, respectively. Media 44 and 64 are preferably non-transitory storage media storing computer code instructions for executing the method of the present embodiments, and processors 32 and 52 execute these code instructions. The code instructions can be run by loading the respective code instructions into the respective execution memories 38 and 58 of the respective processors 32 and 52. Storage media 64 preferably also store one or more databases including a database of psychologically annotated olfactory perception signatures as further detailed hereinabove.

In operation, processor 32 of client computer 30 displays on GUI 42 a questionnaire and a set of questionnaire controls, such as, but not limited to, a slider, a dropdown menu, a combo box, a text box and the like. A representative example of a displayed questionnaire 60 and a set of controls 62 is shown in FIG. 6C. A person on behalf of the subject can enter response parameters using the questionnaire controls displayed on GUI 42.

Processor 32 receives the response parameters from GUI 42 and typically transmits these parameters to server computer 50 over network 40. Media 64 can store a machine learning procedure trained for predicting likelihoods for childhood obesity. Server computer 50 can access media 64, feed the stored procedure with the parameters received from client computer 30, and receive from the procedure an output indicative of the likelihood that the subject that is characterized by the parameters is expected to develop childhood obesity. Server computer 50 can also transmit to client computer 30 the obtained likelihood, and client computer 30 can display this information on GUI 42.

As used herein the term “about” refers to ±10%.

The word “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments.

The word “optionally” is used herein to mean “is provided in some embodiments and not provided in other embodiments.” Any particular embodiment of the invention may include a plurality of “optional” features unless such features conflict.

The terms “comprises”, “comprising”, “includes”, “including”, “having” and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, method or structure may include additional ingredients, steps and/or parts, but only if the additional ingredients, steps and/or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.

As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

As used herein the term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.

As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with the above descriptions illustrate some embodiments of the invention in a non limiting fashion.

Example 1

Table 1.1 presents a list of 945 parameters from which parameters for feeing the machine learning procedure can be selected when the subject is an infant or toddler subject. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.1, than a parameter that is listed lower in Table 1.1. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.1, where N≤M≤945.

TABLE 1.1
No.	Parameter

1	Last WFL zscore
2	Weight Routine checkup - 18-22 months
3	Weight Routine checkup - 12-16 months
4	WFL zscore median
5	Siblings median BMI zscore mean
6	WFL zscore mean
7	Weight Routine checkup - 4-6 months
8	Ethnicity: North Africa
9	Siblings mean BMI zscore mean
10	Siblings max BMI zscore mean
11	Father BMI median
12	WFL Routine checkup - 18-22 months
13	WFL zscore max
14	Father BMI max
15	Child mean Hb
16	Siblings at 5 years of age BMI zscore mean
17	Siblings min BMI zscore mean
18	Father BMI mean
19	Child mean Mpv
20	Father BMI min
21	Mother Pre-Pregnancy BMI max
22	Child All Antibiotics prescription day counts
23	Weight Routine checkup - 9-12 months
24	Mother Pre-Pregnancy BMI median
25	Child diagnosed Acute upper respiratory infections of multiple or
	unsp.sites
26	Mother 24-40 weeks MCV
27	Height Routine checkup - 12-16 months
28	Mother Pre-Pregnancy BMI mean
29	Child mean Baso %
30	Mother 24-40 weeks MCH
31	Child mean Rdw
32	Child mean Plt
33	Child count Salbutamol
34	Height Routine checkup - 18-22 months
35	Weight Routine checkup - 6-9 months
36	Age of Father at birth
37	Child mean Eosinophils abs-dif
38	Siblings count BMI zscore std
39	Mother Pre-Pregnancy BMI min
40	WFL Routine checkup - 1-2 months
41	Ethnicity: Ethiopia
42	Weight Routine checkup - 2-3 months
43	Child mean Mcv
44	Child count Bethamethasone
45	Mother last BMI 24-40 weeks
46	Age of Mother at birth
47	WFL Routine checkup - 9-12 months
48	WFL zscore slope
49	Father Weight median
50	WFL Routine checkup - 12-16 months
51	Locality type: Jewish Locality 100,000-199,999 residents
52	Age at last WFL
53	Mother Pre-Pregnancy Weight max
54	Ethnicity: Unknown
55	Weight Routine checkup - 1-2 months
56	Mother last BMI 0-12 weeks
57	WFL zscore intercept
58	Height Routine checkup - 4-6 months
59	Child diagnosed Nausea and vomiting
60	Ethnicity: North America
61	Father Height median
62	Height Routine checkup - 6-9 months
63	Mother Pre-Pregnancy Weight mean
64	Ethnicity: West Europe
65	Child mean Hct
66	Locality type: Non-Jewish Locality 5,000-9,999 residents
67	Child mean Ggt
68	Mother 12-24 weeks VITAMIN B12
69	Child diagnosed Dyspnea and respiratory abnormalities
70	Mother 0-12 weeks MCH
71	Child mean Mch
72	Father std Cholesterol
73	Child mean Wbc
74	Child diagnosed Colitis, enteritis, gastroenteritis presumed
	infectious origin
75	Child diagnosed Acute upper respiratory infections of unspecified
	site
76	Mother Pre-Pregnancy Weight median
77	Siblings min BMI zscore std
78	Child mean Protein-total
79	Week of year born
80	Child mean Hypo %
81	Mother Pre-Pregnancy Weight min
82	WFL zscore min
83	Child diagnosed Hypertrophy of tonsils and adenoids
84	Mother Pre-pregnancy CMV IgG
85	Mother Pre-pregnancy PDW
86	Child diagnosed Acute tonsillitis
87	Mother 24-40 weeks GLUCOSE 50 g
88	Mother Pre-pregnancy GGT
89	Child mean Gpt (alt)
90	Child mean Albumin
91	Child diagnosed Fever
92	Child mean Ferritin
93	Father Height mean
94	Height Routine checkup - 9-12 months
95	Ethnicity: Iraq
96	Siblings mean BMI zscore std
97	Child count Budesonide
98	Father max Triglycerides
99	Mother 12-24 weeks RBC
100	Mother 0-12 weeks WBC
101	Siblings std BMI zscore mean
102	Mother last Diastolic Blood Pressure 24-40 weeks
103	Mother 12-24 weeks HB
104	Mother 12-24 weeks LUC %
105	Child Penicillin Antibiotics prescription day counts
106	Child mean Ldh
107	Mother 0-12 weeks VITAMIN B12
108	Child diagnosed Lack of coordination
109	Mother 0-12 weeks HCT
110	Mother Pre-pregnancy GLUCOSE 50 g
111	Father mean Cholesterol- hdl
112	Father mean Triglycerides
113	Father Height min
114	Child mean Tsh
115	Siblings count BMI zscore mean
116	Mother 0-12 weeks LYMP.abs
117	Child mean Rdw-cv
118	WFL Routine checkup - 6-9 months
119	Locality type: Non-Jewish Locality 10,000-19,999 residents
120	Mother Pre-pregnancy GLUCOSE
121	Child diagnosed Acute bronchiolitis
122	Mother last BMI 12-24 weeks
123	Father std Glucose
124	Mother Pre-pregnancy CK—CREAT.KINASE(CPK)
125	Child mean Creatinine
126	Father std Cholesterol-ldl calc
127	Father min Cholesterol- hdl
128	Mother last BMI Pre-pregnancy
129	Mother Pre-pregnancy TSH
130	Date of Birth
131	Mother last Weight Pre-pregnancy
132	Mother Pre-pregnancy MCHC
133	Mother Pre-pregnancy LYMP.abs
134	Siblings median BMI zscore std
135	Mother 12-24 weeks IRON
136	Mother count Roxithromycin
137	Mother last Weight 12-24 weeks
138	Mother 24-40 weeks MPV
139	Mother 12-24 weeks GLUCOSE
140	Mother Pre-pregnancy PT %
141	Height Routine checkup - 2-3 months
142	Mother 24-40 weeks VITAMIN B12
143	Father max Glucose
144	Father Weight max
145	Mother 24-40 weeks EOS %
146	Child diagnosed Cough
147	Child count Amoxicillin
148	Mother 24-40 weeks GLUCOSE (GTT) 0′
149	Mother Pre-pregnancy HCT
150	Mother Pre-pregnancy BILIRUBIN-DIRECT
151	Age at Target measurement
152	Mother 0-12 weeks MPV
153	Ethnicity: East Europe
154	Siblings max BMI zscore std
155	Child mean Glucose
156	Child mean Stabs %-dif
157	Height Routine checkup - 1-2 months
158	Father mean Glucose
159	Child mean Mono %
160	Mother 0-12 weeks NEUT.abs
161	Child mean Neutrophils abs-dif
162	Father Weight mean
163	Mother Pre-pregnancy T4- FREE
164	WFL zscore slope_std_err
165	Mother 24-40 weeks RBC
166	Mother Pre-pregnancy LYM %
167	Child diagnosed Hearing loss
168	Child mean Eos.abs
169	Child mean Sodium
170	Mother 24-40 weeks ALK. PHOSPHATASE
171	Child diagnosed Urinary tract infection, site not specified
172	Child mean Luc abs
173	Mother 0-12 weeks EOS.abs
174	Father min Triglycerides
175	Mother 0-12 weeks MONO.abs
176	Child mean Luc %
177	Mother Pre-pregnancy MPV
178	Mother Pre-pregnancy NEUT %
179	Mother 24-40 weeks APTT-R
180	Child diagnosed Otorrhea
181	Siblings at 13 years of age BMI zscore mean
182	Ethnicity: Muslim Arab
183	Child mean Atypical lymph.%-dif
184	Mother Pre-pregnancy PHOSPHORUS
185	WFL Routine checkup - 2-3 months
186	Father count Metformin
187	WFL zscore count
188	Child mean T4- free
189	Mother Pre-pregnancy NEUT.abs
190	Mother 12-24 weeks MCHC
191	Child mean Chloride
192	Mother 24-40 weeks HEMOGLOBIN A1C %
193	Mother Pre-pregnancy CHOLESTEROL-LDL calc
194	Child mean Lym %
195	Child mean Mono.abs
196	Child diagnosed Sleep disturbances
197	Child mean Micro %
198	Child mean Calcium
199	Child mean Rbc
200	Mother last Systolic Blood Pressure 0-12 weeks
201	Child mean Lymphocytes abs-dif
202	WFL Routine checkup - 4-6 months
203	Father median Triglycerides
204	Mother 24-40 weeks MICRO %
205	Mother last Systolic Blood Pressure 12-24 weeks
206	Mother 24-40 weeks MONO.abs
207	Mother 12-24 weeks PLT
208	Locality type: Jewish Locality 10,000-19,999 residents
209	Child mean Alk. phosphatase
210	Child mean Baso abs
211	Child mean Eos %
212	Mother Pre-pregnancy LDH
213	Child mean Atypical lymph-dif
214	Mother 0-12 weeks HEPATITIS Bs Ab
215	Child mean Hyper %
216	Child mean Got (ast)
217	Mother Pre-pregnancy PLT
218	Father min Glucose
219	Child mean Lymp.abs
220	Father max Non-hdl_cholesterol
221	Mother 12-24 weeks NEUT %
222	Mother 24-40 weeks HYPO %
223	Mother last Systolic Blood Pressure Pre-pregnancy
224	Father Height max
225	Mother last Systolic Blood Pressure 24-40 weeks
226	Father median Cholesterol- hdl
227	Mother 12-24 weeks T4- FREE
228	Mother Pre-pregnancy UREA
229	Mother Pre-pregnancy MAGNESIUM
230	Mother 0-12 weeks CHOLESTEROL/HDL
231	Child mean Mchc
232	Mother 24-40 weeks LYM %
233	Mother 12-24 weeks MCV
234	Mother Pre-pregnancy MONO.abs
235	Child mean Neut.abs
236	Mother Pre-pregnancy WBC
237	Mother 12-24 weeks MONO.abs
238	Mother 24-40 weeks HCT
239	Mother 0-12 weeks CMV IgG
240	Mother 24-40 weeks PLT
241	WFL zscore std
242	Birth weight
243	Mother Pre-pregnancy PROTEIN-TOTAL
244	Mother 12-24 weeks CMV IgG
245	Child mean Cholesterol
246	Mother 24-40 weeks CMV IgG
247	Mother 0-12 weeks SODIUM
248	Mother 24-40 weeks NEUT %
249	Mother 24-40 weeks MCHC
250	Father Weight min
251	Mother count Amoxicillin
252	Father mean Cholesterol
253	Child mean Bilirubin total
254	Father median Glucose
255	Child mean Pdw
256	Mother Pre-pregnancy CHOLESTEROL
257	Child Macrolides Antibiotics prescription day counts
258	Mother 0-12 weeks MONO %
259	Mother 24-40 weeks LYMP.abs
260	Mother 12-24 weeks NEUT.abs
261	Mother Pre-pregnancy HYPER %
262	Child mean Iron
263	Mother 12-24 weeks TSH
264	Mother count Cabergoline
265	Mother last Weight 0-12 weeks
266	Mother Pre-pregnancy PCT
267	Father Height std
268	Mother 0-12 weeks TRIGLYCERIDES
269	Mother 0-12 weeks GLUCOSE
270	Father std Cholesterol/hdl
271	Mother Pre-pregnancy HYPO %
272	Mother 24-40 weeks FERRITIN
273	Child count Terbutaline
274	Child mean Monocytes %-dif
275	Jewish Locality
276	Child mean Uric acid
277	Child diagnosed Acute nonsuppurative otitis media
278	Father BMI std
279	Mother Pre-pregnancy BASO %
280	Mother 24-40 weeks SODIUM
281	Mother Pre-pregnancy VITAMIN B12
282	Mother 0-12 weeks ESTRADIOL (E-2)
283	Mother 0-12 weeks LYM %
284	Mother 12-24 weeks EOS %
285	Mother 24-40 weeks NEUT.abs
286	Mother 24-40 weeks NEUTROPHILS abs-DIF
287	Father diagnosed Diabetes mellitus
288	Mother Pre-pregnancy CREATININE
289	Child Cephalosporin Antibiotics prescription day counts
290	Father Weight std
291	Mother 24-40 weeks HB
292	Mother BMI delta 12-24 weeks to 24-40 weeks
293	Mother 0-12 weeks GGT
294	Child mean Urea
295	Mother 0-12 weeks LH
296	Mother 24-40 weeks RDW
297	Mother 12-24 weeks HbA2
298	Mother 0-12 weeks MCV
299	Mother Pre-pregnancy MONO %
300	Mother Pre-pregnancy HB
301	Child mean Micro %/hypo %
302	Mother 24-40 weeks LUC %
303	Mother count Enoxaparin
304	Child mean Monocytes abs-dif
305	Mother 24-40 weeks MONO %
306	Mother 0-12 weeks NEUT %
307	Mother 24-40 weeks WBC
308	Child diagnosed Acute conjunctivitis
309	Father mean Non-hdl_cholesterol
310	Child mean Neutrophils %-dif
311	Mother 0-12 weeks EOS %
312	Mother 0-12 weeks RDW
313	Mother Pre-pregnancy RDW
314	Mother 12-24 weeks LYM %
315	Mother Pre-pregnancy SHBG
316	Mother Pre-pregnancy FOLIC ACID
317	Child mean Transferrin
318	Child diagnosed Other viral diseases; nos
319	Mother 0-12 weeks HYPO %
320	Mother Pre-pregnancy MICRO %
321	Mother 24-40 weeks BILIRUBIN TOTAL
322	Child mean Lymphocytes %-dif
323	Mother Pre-pregnancy SODIUM
324	Mother Pre-pregnancy RBC
325	Child diagnosed Teething syndrome
326	Child count Prednisolone
327	Mother 24-40 weeks BASO %
328	Mother 24-40 weeks LYMPHOCYTES abs-DIF
329	Mother 0-12 weeks PROGESTERONE
330	Father BMI count
331	Mother Pre-pregnancy TRIGLYCERIDES
332	Father max Cholesterol
333	Mother 12-24 weeks LYMP.abs
334	Child diagnosed Benign neoplasm of skin, site unspecified
335	Mother last Diastolic Blood Pressure 0-12 weeks
336	Mother Pre-pregnancy GLOBULIN
337	Mother 24-40 weeks CREATININE
338	Father max Cholesterol-ldl calc
339	Father max Cholesterol- hdl
340	Mother Pre-pregnancy ESR
341	Mother 12-24 weeks PT-SEC
342	Mother 24-40 weeks LUC abs
343	Mother 24-40 weeks MPXI
344	Mother Pre-Pregnancy BMI std
345	Mother 12-24 weeks FERRITIN
346	Mother 0-12 weeks MPXI
347	Mother 0-12 weeks TSH
348	Mother 24-40 weeks GOT (AST)
349	Mother 24-40 weeks HYPER %
350	Mother 24-40 weeks EOSINOPHILS abs-DIF
351	Mother 12-24 weeks WBC
352	Father mean Cholesterol-ldl calc
353	Ethnicity: Iran
354	Child count Dimethindene
355	Father std Triglycerides
356	Mother Pre-pregnancy HDW
357	Mother 0-12 weeks UREA
358	Mother 12-24 weeks HCT
359	Mother Pre-pregnancy HEPATITIS Bs Ab
360	Child mean Triglycerides
361	Child diagnosed Acute lymphadenitis
362	Mother 0-12 weeks LDH
363	Mother 12-24 weeks POTASSIUM
364	Child mean Neut %
365	Child diagnosed Unspecified fetal and neonatal jaundice
366	Mother Pre-Pregnancy Weight std
367	Mother 12-24 weeks MICRO %
368	Mother Pre-pregnancy BILIRUBIN TOTAL
369	Mother 0-12 weeks HB
370	Child mean Mpxi
371	Mother Pre-pregnancy C-REACTIVE PROTEIN
372	Mother Pre-pregnancy MCV
373	Mother Pre-pregnancy DHEA SULPHATE
374	Child mean Pct
375	Father min Cholesterol
376	Locality type: Jewish Locality 50,000-99,999 residents
377	Mother Pre-pregnancy EOS %
378	Father median Cholesterol
379	Child mean Hct/hgb ratio
380	Mother 24-40 weeks BILIRUBIN-DIRECT
381	Child diagnosed Diaper or napkin rash
382	Mother 24-40 weeks STABS %-DIF
383	Child mean Stabs abs-dif
384	Siblings at 5 years of age BMI zscore std
385	Child diagnosed Congenital anomalies of lower limb, including
	pelvic girdle
386	Father std Cholesterol- hdl
387	Child count Cefalexin
388	Mother 12-24 weeks HYPO %
389	Child diagnosed Oral aphthae
390	Mother 24-40 weeks STABS abs-DIF
391	Child mean Phosphorus
392	Mother 0-12 weeks LUC %
393	Mother 12-24 weeks SODIUM
394	Mother 24-40 weeks GLUCOSE (GTT) 60′
395	Mother 24-40 weeks CHOLESTEROL
396	Child count Erythromycin
397	No. of Siblings with BMI data
398	Mother 12-24 weeks CREATININE
399	Mother 24-40 weeks GLUCOSE (GTT) 180′
400	Mother 12-24 weeks EOS.abs
401	Child diagnosed Asthma
402	Mother Pre-pregnancy COMPLEMENT C3
403	Mother Pre-pregnancy EOS.abs
404	Ethnicity: Asian
405	Mother 24-40 weeks T3- FREE
406	Mother Pre-pregnancy FERRITIN
407	Mother Pre-pregnancy AMYLASE
408	Father count Pravastatin
409	Mother 24-40 weeks MONOCYTES abs-DIF
410	Mother 24-40 weeks GPT (ALT)
411	Mother Pre-pregnancy URIC ACID
412	Father diagnosed Obesity, unspecified
413	Mother 24-40 weeks NEUTROPHILS %-DIF
414	Child diagnosed Bronchopneumonia, organism unspecified
415	Mother 0-12 weeks MCHC
416	Mother 12-24 weeks MONO %
417	Mother Pre-pregnancy FIBRINOGEN CALCU
418	Mother Pre-pregnancy MPXI
419	Child Beta lactam Penicillin Antibiotics prescription day counts
420	Mother 0-12 weeks URIC ACID
421	Mother Pre-pregnancy LH
422	Mother 24-40 weeks MACRO %
423	Mother Pre-pregnancy MCH
424	Mother 24-40 weeks BASO abs
425	Father count Cholesterol-ldl calc
426	Mother 0-12 weeks MICRO %
427	Mother Weight delta Pre-pregnancy to 0-12 weeks
428	Child diagnosed Constipation
429	Siblings std BMI zscore std
430	Mother 24-40 weeks LDH
431	Mother 0-12 weeks PLT
432	Siblings at 13 years of age BMI zscore std
433	Father count Glucose
434	Mother Pre-pregnancy BILIRUBIN INDIRECT
435	Child mean Eosinophils %-dif
436	Mother 24-40 weeks URIC ACID
437	Mother BMI delta Pre-pregnancy to 0-12 weeks
438	Mother 12-24 weeks GGT
439	Mother 0-12 weeks GPT (ALT)
440	Mother 0-12 weeks PHOSPHORUS
441	Mother Pre-pregnancy LUC %
442	Child diagnosed U.r.i. (head cold)
443	Mother 0-12 weeks HYPER %
444	Mother 0-12 weeks CREATININE
445	Mother 12-24 weeks MICRO %/HYPO %
446	Mother 0-12 weeks MACRO %
447	Mother 12-24 weeks RDW
448	Mother Pre-pregnancy POTASSIUM
449	Mother 0-12 weeks RBC
450	Mother Pre-pregnancy ALK. PHOSPHATASE
451	Child diagnosed Enlargement of lymph nodes
452	Mother Pre-pregnancy ALBUMIN
453	Mother 12-24 weeks TRIGLYCERIDES
454	Mother 0-12 weeks AMYLASE
455	Father min Cholesterol-ldl calc
456	Mother 0-12 weeks ALK. PHOSPHATASE
457	Mother Pre-pregnancy PT-SEC
458	Child diagnosed Diarrhea
459	Mother 0-12 weeks VITAMIN D (25-OH)
460	Child diagnosed Pneumonia
461	Mother 12-24 weeks MCH
462	Child mean Potassium
463	Mother Pre-pregnancy CALCIUM
464	Father count Cholesterol- hdl
465	Father median Cholesterol-ldl calc
466	Mother Pre-pregnancy COMPLEMENT C4
467	Mother count Ofloxacin
468	Child mean C-reactive protein
469	Mother last Weight 24-40 weeks
470	Mother 0-12 weeks CHOLESTEROL-LDL calc
471	Mother Pre-pregnancy MACRO %
472	Mother count Phenoxymethylpenicillin
473	Mother 0-12 weeks HDW
474	Mother 24-40 weeks TRIGLYCERIDES
475	Mother Pre-pregnancy TESTOSTERONE- TOTAL
476	Father std Non-hdl_cholesterol
477	Child diagnosed Contusion of unspecified site
478	Mother 0-12 weeks NON-HDL_CHOLESTEROL
479	Child diagnosed Esophagitis
480	Child mean Macro %
481	Mother last Diastolic Blood Pressure Pre-pregnancy
482	Mother 0-12 weeks APTT-sec
483	Child count Cefuroxime
484	Child diagnosed Atopic dermatitis/eczema
485	Mother 24-40 weeks MICRO %/HYPO %
486	Ethnicity: USSR
487	Mother 12-24 weeks MPXI
488	Mother 0-12 weeks BASO %
489	Father min Non-hdl_cholesterol
490	Mother Pre-pregnancy NON-HDL_CHOLESTEROL
491	Mother 0-12 weeks GLOBULIN
492	Mother 12-24 weeks MACRO %
493	Child diagnosed Stridor
494	Father count Simvastatin
495	Mother 12-24 weeks LUC abs
496	Child diagnosed Infectious diarrhea
497	Mother 12-24 weeks PT-INR
498	Mother 0-12 weeks GOT (AST)
499	Father min Cholesterol/hdl
500	Mother 24-40 weeks GLUCOSE
501	Mother 24-40 weeks EOS.abs
502	Child diagnosed Chronic rhinitis
503	Mother 12-24 weeks UREA
504	Mother 0-12 weeks PROTEIN-TOTAL
505	Mother Pre-pregnancy ALY
506	Mother Pre-pregnancy FREE ANDROGEN INDEX
507	Child diagnosed Unsp.viral infect.in conditions classif.elsewhere,
	unsp.site
508	Mother 0-12 weeks POTASSIUM
509	Mother 12-24 weeks AMYLASE
510	Mother 12-24 weeks CK—CREAT.KINASE(CPK)
511	Mother Pre-pregnancy GPT (ALT)
512	Mother 0-12 weeks CHOLESTEROL
513	Mother 12-24 weeks BASO %
514	Child diagnosed Anorexia
515	Mother Pre-pregnancy CORTISOL-BLOOD
516	Mother 24-40 weeks RDW-CV
517	Mother Pre-pregnancy ESTRADIOL (E-2)
518	Mother 12-24 weeks MPV
519	Child diagnosed Other specified disease of white blood cells
520	Mother Pre-pregnancy PROLACTIN
521	Mother 24-40 weeks TSH
522	is Male
523	Child diagnosed Lack of expected normal physiological
	development
524	Mother 0-12 weeks CK—CREAT.KINASE(CPK)
525	Father median Non-hdl_cholesterol
526	Father mean Cholesterol/hdl
527	Mother 0-12 weeks FOLIC ACID
528	Mother 24-40 weeks IRON
529	Mother 0-12 weeks LUC abs
530	Mother Pre-pregnancy RUBELLA Ab IgG
531	Mother 0-12 weeks ALBUMIN
532	Child mean Bilirubin-direct
533	Mother 0-12 weeks IRON
534	Mother 0-12 weeks RUBELLA Ab IgG
535	Mother 24-40 weeks AMYLASE
536	Number of twin siblings
537	Mother Pre-pregnancy ANDROSTENEDIONE
538	Father count Enalapril
539	Mother count Mebendazole
540	Mother 24-40 weeks CHLORIDE
541	Child diagnosed Influenza
542	Child count Desloratadine
543	Mother 24-40 weeks HDW
544	Child count Ketotifen
545	Child diagnosed Dermatitis due to food taken internally
546	Mother 24-40 weeks GLUCOSE (GTT) 120′
547	Father count Cholesterol
548	Mother 12-24 weeks PCT
549	Mother 24-40 weeks UREA
550	Child count Ipratropium bromide
551	Child diagnosed Acute pharyngitis
552	Child diagnosed Acute suppurative otitis media
553	Mother 0-12 weeks TOXOPLASMA IgG
554	Mother Pre-pregnancy MICRO %/HYPO %
555	Mother 24-40 weeks PROTEIN-TOTAL
556	Mother 12-24 weeks TOXOPLASMA IgG
557	Mother 0-12 weeks FSH
558	Father count Non-hdl_cholesterol
559	Child diagnosed Acute nasopharyngitis (common cold)
560	Mother 24-40 weeks CHOLESTEROL- HDL
561	Mother 24-40 weeks PT-SEC
562	Mother Pre-pregnancy ANTI CARDIOLIPIN IgG
563	Mother Pre-Pregnancy BMI count
564	Mother 24-40 weeks PDW
565	Mother 24-40 weeks MONOCYTES %-DIF
566	Mother 0-12 weeks MICRO %/HYPO %
567	Mother Pre-pregnancy TRANSFERRIN
568	Mother Pre-pregnancy GOT (AST)
569	Child diagnosed Other diseases of conjunctiva due to viruses and
	chlamydiae
570	Mother Pre-pregnancy PT-INR
571	Mother 24-40 weeks CALCIUM
572	Child diagnosed Other atopic dermatitis and related conditions
573	Mother 0-12 weeks HEMOGLOBIN A
574	Mother Pre-pregnancy LUC abs
575	Father count Amlodipine
576	Mother 12-24 weeks ALK. PHOSPHATASE
577	Father count Triglycerides
578	Mother 0-12 weeks CALCIUM
579	Child count Azithromycin
580	Mother 12-24 weeks FOLIC ACID
581	Mother Pre-pregnancy FSH
582	Child diagnosed Pneumonia, organism unspecified
583	Mother Pre-pregnancy CHOLESTEROL- HDL
584	Locality type: Non-Jewish Other Rural Locality
585	Child count Ahiston drop cd
586	Mother Pre-pregnancy PROGESTERONE
587	Mother 0-12 weeks T4- FREE
588	Mother 12-24 weeks BASO abs
589	Child diagnosed Other and unspec.noninfectious gastroenteritis
	and colitis
590	Child diagnosed Asthma, unspecified
591	Mother Pre-pregnancy ANTITHROMBIN-III
592	Mother 24-40 weeks TOXOPLASMA IgG
593	Mother 0-12 weeks PT-SEC
594	Child diagnosed Volume depletion disorder
595	Mother Pre-pregnancy CONTROL PTT
596	Mother 24-40 weeks EOSINOPHILS %-DIF
597	Mother Pre-pregnancy 17-OH-PROGESTERONE
598	Father count Cholesterol/hdl
599	Mother Pre-pregnancy IRON
600	Mother Pre-pregnancy HEMOGLOBIN A1C %
601	Mother 12-24 weeks HYPER %
602	Mother 0-12 weeks BASO abs
603	Locality type: Non-Jewish Locality 2,000-4,999 residents
604	Mother Pre-pregnancy APTT-sec
605	Mother count Fluticasone
606	Mother 24-40 weeks HCT/HGB Ratio
607	Father count Bezafibrate
608	Locality type: Jewish Locality 200,000-499,999 residents
609	Father diagnosed Obesity (bmi >30)
610	Mother count Omeprazole
611	Child count Co-amoxiclav cd
612	Mother 24-40 weeks PT-INR
613	Mother Pre-pregnancy HCT/HGB Ratio
614	Child count Montelukast
615	Child diagnosed Infectious colitis, enteritis, and gastroenteritis
616	Mother Pre-Pregnancy Weight count
617	Mother count Estradiol
618	Mother 24-40 weeks PCT
619	Mother Pre-pregnancy T3-TOTAL
620	Mother count Follitropin alfa
621	Child diagnosed Acute bronchitis
622	Ethnicity: Yemen
623	Child diagnosed Abdominal pain
624	Child diagnosed Other and unspecified injury to unspecified site
625	Child count Prothiazine/promethazine expectorant cd
626	Mother 24-40 weeks PT %
627	Locality type: Moshav
628	Mother Pre-pregnancy VLDL
629	Mother 24-40 weeks POTASSIUM
630	Child count Co-trimoxazole cd
631	Mother 12-24 weeks HbF
632	Mother 24-40 weeks BILIRUBIN INDIRECT
633	Mother 24-40 weeks GLOM.FILTR.RATE
634	Mother 24-40 weeks PHOSPHORUS
635	Father max Cholesterol/hdl
636	Child diagnosed Iron deficiency anemia, unspecified
637	Mother Pre-pregnancy ALY %
638	Child diagnosed Rash and other nonspecific skin eruption
639	Mother 0-12 weeks PT %
640	Mother 12-24 weeks PT %
641	Mother 24-40 weeks TRANSFERRIN
642	Father Weight count
643	Child diagnosed Late effect of injury to cranial nerve
644	Mother Pre-pregnancy T3- FREE
645	Mother 12-24 weeks PROTEIN-TOTAL
646	Cesarean birth
647	Mother Pre-pregnancy BASO abs
648	Mother 0-12 weeks T3- FREE
649	Mother Pre-pregnancy RDW-CV
650	Mother count Levothyroxine sodium
651	Child Sulfonamides Antibiotics prescription day counts
652	Mother 12-24 weeks ALBUMIN
653	Child diagnosed Undescended testicle
654	Mother 12-24 weeks CHOLESTEROL
655	Child diagnosed Hearing complaints
656	Mother 24-40 weeks MAGNESIUM
657	Mother 0-12 weeks PDW
658	Mother 0-12 weeks TRANSFERRIN
659	Mother 24-40 weeks HbA2
660	Mother 12-24 weeks T3- FREE
661	Mother count Aspirin
662	Mother 0-12 weeks BLOOD TYPE
663	Mother count Human menopausal gonadotrophin
664	Mother count Co-amoxiclav cd
665	Mother 24-40 weeks T4- FREE
666	Child diagnosed Contact dermatitis and other eczema, unspecified
	cause
667	Mother 0-12 weeks DHEA SULPHATE
668	Child diagnosed Intestinal malabsorption
669	Mother 0-12 weeks PROLACTIN
670	Child diagnosed Blepharitis
671	Mother 24-40 weeks LYMPHOCYTES %-DIF
672	Mother 0-12 weeks FERRITIN
673	Mother count Symbicort/duoresp
674	Mother Pre-pregnancy PROTEIN C ACTIVITY
675	Mother 0-12 weeks HCT/HGB Ratio
676	Mother Pre-pregnancy CHOLESTEROL/HDL
677	Child count Metronidazole
678	Mother 12-24 weeks NORMOBLAST.abs
679	Father median Cholesterol/hdl
680	Mother 24-40 weeks ALBUMIN
681	Child diagnosed Candidiasis of skin and nails
682	Mother last Diastolic Blood Pressure 12-24 weeks
683	Mother 0-12 weeks RDW-CV
684	Mother 12-24 weeks URIC ACID
685	Apidoral given at birth
686	Mother 12-24 weeks BILIRUBIN TOTAL
687	Child diagnosed Irritable infant
688	Child diagnosed Varicella without mention of complication
689	Mother 0-12 weeks BILIRUBIN TOTAL
690	Father diagnosed Other and unspecified hyperlipidemia
691	Child diagnosed Infective otitis externa
692	Child diagnosed Insect bite
693	Mother Pre-pregnancy ANTI CARDIOLIPIN IgM
694	Child diagnosed Stenosis and insufficiency of lacrimal passages
695	Mother 24-40 weeks APTT-sec
696	Mother 24-40 weeks VITAMIN D (25-OH)
697	Mother 24-40 weeks GLOBULIN
698	Mother Pre-pregnancy CA-125
699	Child diagnosed Acute and unspecified inflammation of lacrimal
	passages
700	Mother count Cetirizine
701	Child diagnosed Anal fissure
702	Child diagnosed Impetigo
703	Child diagnosed Laceration/cut
704	Mother 12-24 weeks APTT-sec
705	Mother 12-24 weeks LDH
706	Child diagnosed Contact dermatitis and other eczema
707	Mother 24-40 weeks CK—CREAT.KINASE(CPK)
708	Child diagnosed Serous otitis media; glue
709	Mother 0-12 weeks BILIRUBIN-DIRECT
710	Mother 12-24 weeks GPT (ALT)
711	Child count Fluticasone
712	Mother Pre-pregnancy APTT-R
713	Mother 24-40 weeks FIBRINOGEN CALCU
714	Mother 12-24 weeks NORMOBLAST.%
715	Child diagnosed Injuries
716	Mother 0-12 weeks CHOLESTEROL- HDL
717	Mother count Desogestrel
718	Mother Pre-pregnancy EOSINOPHILS %-DIF
719	Child diagnosed Wheezing baby syndrome
720	Mother 24-40 weeks FOLIC ACID
721	Mother Pre-pregnancy IgA
722	Child diagnosed Croup
723	Mother Pre-pregnancy PROT-S ANTIGEN (FREE
724	Mother count Lansoprazole
725	Mother 12-24 weeks CHOLESTEROL-LDL calc
726	Child diagnosed Diseases and other conditions of the tongue
727	Mother 12-24 weeks ALPHA FETOPROTEIN TM
728	Mother 12-24 weeks GLUCOSE 50 g
729	Mother 0-12 weeks HbF
730	Locality type: Collective Moshav
731	Child diagnosed Abnormal loss of weight
732	Child diagnosed Other diseases of nasal cavity and sinuses
733	Mother BMI delta 0-12 weeks to 12-24 weeks
734	Mother 0-12 weeks BILIRUBIN INDIRECT
735	Mother Weight delta 12-24 weeks to 24-40 weeks
736	Child diagnosed Acute laryngitis
737	Locality type: Jewish Locality 20,000-49,999 residents
738	Mother count Cefuroxime
739	Mother 12-24 weeks CALCIUM
740	Father diagnosed Essential hypertension
741	Mother Pre-pregnancy MONOCYTES abs-DIF
742	Child diagnosed Umbilical hernia without mention of
	obstruction or gangrene
743	Child diagnosed Allergy/allergic react nos
744	Child diagnosed Congenital musculoskeletal deformities of
	sternocleidomastoid
745	Child diagnosed Other speech disturbance
746	Mother 12-24 weeks RDW-CV
747	Mother 0-12 weeks PCT
748	Mother Pre-pregnancy LYMPHOCYTES %-DIF
749	Mother 24-40 weeks NORMOBLAST.abs
750	Child diagnosed Enterobiasis
751	Mother Pre-pregnancy FIBRINOGEN
752	Mother count Cefalexin
753	Child count Ceftriaxone
754	Mother Pre-pregnancy CHLORIDE
755	Mother count Progesterone
756	Locality type: Jewish Other Rural Locality
757	Child diagnosed Other and unspecified chronic nonsuppurative
	otitis media
758	Mother 12-24 weeks GOT (AST)
759	Mother 12-24 weeks PDW
760	Locality type: Jewish Locality 2,000-4,999 residents
761	Father diagnosed Morbid obesity
762	Mother Pre-pregnancy BLOOD TYPE
763	Mother 0-12 weeks HbA2
764	Mother Weight delta 0-12 weeks to 12-24 weeks
765	Mother 24-40 weeks NON-HDL_CHOLESTEROL
766	Mother 12-24 weeks HDW
767	Mother Pre-pregnancy GLOM.FILTR.RATE
768	Child diagnosed Otalgia
769	Child diagnosed Unspecified otitis media
770	Premature birth
771	Child diagnosed Unsp.adv.effect of drug, medicinal/biological
	substance n.e.s.
772	Mother Pre-pregnancy VITAMIN D (25-OH)
773	Mother 24-40 weeks CHOLESTEROL-LDL calc
774	Mother 12-24 weeks CHLORIDE
775	Born in Israel
776	Mother 12-24 weeks CHOLESTEROL- HDL
777	Mother Pre-pregnancy HbA2
778	Mother 0-12 weeks CHLORIDE
779	Locality type: Communal Locality
780	Mother Pre-pregnancy LIC
781	Locality type: Jewish Locality 5,000-9,999 residents
782	Mother 24-40 weeks NORMOBLAST.%
783	Locality type: Jewish Locality 500,000 and more residents
784	Locality type: Kibbutz
785	Locality type: Moshav 2,000-4,999 residents
786	Mother 0-12 weeks NORMOBLAST.%
787	Mother Pre-pregnancy NORMOBLAST.%
788	Locality type: Non-Jewish Locality 20,000-49,999 residents
789	Child diagnosed Urticaria
790	Mother Pre-pregnancy LIC %
791	Mother 24-40 weeks LI
792	Mother Pre-pregnancy NEUTROPHILS abs-DIF
793	Mother Pre-pregnancy TOXOPLASMA IgG
794	Locality type: Non-Jewish Locality 50,000-99,999 residents
795	Mother 24-40 weeks CONTROL PTT
796	Mother 12-24 weeks NON-HDL_CHOLESTEROL
797	Mother Pre-pregnancy HbF
798	Child diagnosed Vomiting (excl.preg. w06)
799	Mother Pre-pregnancy NEUTROPHILS %-DIF
800	Father Height count
801	Mother Pre-pregnancy MONOCYTES %-DIF
802	Mother Pre-pregnancy LYMPHOCYTES abs-DIF
803	Mother 12-24 weeks PHOSPHORUS
804	Mother 12-24 weeks HbA
805	Mother Pre-pregnancy HEMOGLOBIN A
806	Mother 24-40 weeks GGT
807	Mother 12-24 weeks BILIRUBIN-DIRECT
808	Ethnicity: Africa
809	Mother 0-12 weeks HbA
810	Child diagnosed Viral pneumonia
811	Ethnicity: Mediterranean
812	Child diagnosed Viral exanthem, unspecified
813	Mother 24-40 weeks FIBRINOGEN
814	Ethnicity: Latin America
815	Child diagnosed Torticollis, unspecified
816	Child diagnosed Congenital dislocation of hip
817	Mother 0-12 weeks NORMOBLAST.abs
818	Mother count Carbamazepine
819	Mother count Norgestimate and ethinylestradiol
820	Mother count Norethisterone
821	Mother count Nitrofurantoin
822	Mother count Metronidazole
823	Mother count Methylphenidate
824	Mother count Medroxyprogesterone
825	Mother count Loratadine
826	Mother count Ipratropium bromide
827	Mother count Gestodene and ethinylestradiol
828	Mother count Follitropin beta
829	Mother count Fluoxetine
830	Mother count Fluconazole
831	Mother count Fexofenadine
832	Mother count Famotidine
833	Mother count Escitalopram
834	Mother 0-12 weeks PT-INR
835	Mother count Dydrogesterone
836	Mother count Drospirenone and ethinylestradiol
837	Mother count Doxycycline
838	Mother count Dexamethasone
839	Mother count Desogestrel and ethinylestradiol
840	Mother count Desloratadine
841	Mother count Colchicine
842	Mother count Clonazepam
843	Mother count Clomifene
844	Mother count Clarithromycin
845	Mother count Citalopram
846	Mother count Ciprofloxacin
847	Mother count Chorionic gonadotrophin
848	Mother count Paroxetine
849	Child diagnosed Hand, foot, and mouth disease
850	Mother count Prednisone
851	Mother 12-24 weeks TRANSFERRIN
852	Child diagnosed Chronic serous otitis media
853	Child diagnosed Cellulitis and abscess of unspecified sites
854	Child diagnosed Cellulitis and abscess of finger
855	Child diagnosed Candidiasis of unspecified site
856	Child diagnosed Candidiasis of mouth
857	Child diagnosed Blisters with epidermal loss, burn
	2nd.deg.unspecified site
858	Child diagnosed Convulsions
859	Child diagnosed Delivery in a completely normal case
860	Child diagnosed Anemia other/unspecified
861	Child diagnosed Allergy, unspecified, not elsewhere classified
862	Child diagnosed Allergic rhinitis
863	Child diagnosed Agranulocytosis
864	Child diagnosed Dermatophytosis of the body
865	Child diagnosed Disorders relating to other preterm infants
866	Mother count Progyluton cd
867	Child diagnosed Enteritis due to specified virus
868	Child diagnosed Acute myringitis without mention of otitis media
869	Child diagnosed Acute laryngotracheitis
870	Child diagnosed Feeding difficulties and mismanagement
871	Child diagnosed Acquired deformities of other parts of limbs
872	Child diagnosed Accident/injury; nos
873	Child diagnosed Abnormal weight gain
874	Mother count Triptorelin
875	Mother count Simvastatin
876	Mother count Sertraline
877	Mother count Seretide cd
878	Mother count Salbutamol
879	Child diagnosed Gastrointestinal hemorrhage
880	Mother count Choriogonadotropin alfa
881	Child diagnosed Hemangioma of unspecified site
882	Child diagnosed Tongue tie
883	Mother count Budesonide
884	Child diagnosed Nonsuppurative otitis media, not specified as
	acute or chronic
885	Child diagnosed Open wound of face, without mention of
	complication
886	Mother 12-24 weeks GLOBULIN
887	Child diagnosed Other serum reaction, not elsewhere classified
888	Child diagnosed Other specified erythematous conditions
889	Mother 12-24 weeks BILIRUBIN INDIRECT
890	Child diagnosed Other specified viral exanthemata
891	Child diagnosed Other symptoms involving digestive system
892	Father count Rosuvastatin
893	Father count Ramipril-hydrochlorothiazide cd
894	Father count Ramipril
895	Father count Propranolol
896	Father count Nifedipine-cd
897	Father count Nifedipine
898	Father count Metformin and sitagliptin cd
899	Mother 0-12 weeks GLOM.FILTR.RATE
900	Father count Insulin glargine
901	Child diagnosed Posttraumatic wound infection not elsewhere
	classified
902	Father count Bisoprolol
903	Father count Atorvastatin
904	Father count Atenolol
905	Child diagnosed Premat/immature liveborn infant
906	Child diagnosed Seborrhea
907	Child diagnosed Seborrheic dermatitis, unspecified
908	Mother 12-24 weeks RUBELLA Ab IgG
909	Child diagnosed Sneezing/nasal congestion
910	Child diagnosed Stomatitis
911	Child diagnosed Strabismus and other disorders of binocular eye
	movements
912	Mother Pre-pregnancy NORMOBLAST.abs
913	Child diagnosed Nervousness
914	Child diagnosed Laxity of ligament
915	Mother 0-12 weeks ESR
916	Child diagnosed Hypermetropia
917	Mother count Bethamethasone
918	Mother count Anti-d (rh) immunoglobulin
919	Mother count Aciclovir
920	Child diagnosed Herpangina
921	Mother 12-24 weeks BLOOD TYPE
922	Mother 24-40 weeks BLOOD TYPE
923	Child count Ranitidine
924	Child count Phenoxymethylpenicillin
925	Child count Mebendazole
926	Child count Loratadine
927	Child diagnosed Hip symptoms/complaints
928	Child diagnosed Hydrocele
929	Child diagnosed Hydronephrosis
930	Child count Cefaclor
931	Mother 12-24 weeks HCT/HGB Ratio
932	Child diagnosed Infectious mononucleosis
933	Child count Aciclovir
934	Father diagnosed Unspecified essential hypertension
935	Father diagnosed Overweight (bmi <30)
936	Father diagnosed Other abnormal glucose
937	Father diagnosed Lipid metabolism disorder
938	Father diagnosed Impaired fasting glucose
939	Father diagnosed Disorders of lipoid metabolism
940	Father diagnosed Diabetes mellitus without mention of
	complication
941	Child diagnosed Inguinal hernia, without mention of obstruction or
	gangrene
942	Father diagnosed Adult-onset type diabetes mellitus whithout
	complication
943	Child diagnosed Insect bite, nonvenomous face, neck, scalp
	without infection
944	Child diagnosed Jaundice, unspecified, not of newborn
945	Mother count Lamotrigine

Table 1.2 presents a list of 620 parameters from which parameters for feeing the machine learning procedure can be selected when the subject is when the subject is an unborn subject. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.2, than a parameter that is listed lower in Table 1.2. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.2, where N≤M≤620.

TABLE 1.2
No.	Parameter

1	Siblings median BMI zscore mean
2	Siblings mean BMI zscore mean
3	Siblings max BMI zscore mean
4	Father BMI median
5	Father BMI max
6	Siblings at 5 years of age BMI zscore mean
7	Siblings min BMI zscore mean
8	Father BMI mean
9	Father BMI min
10	Mother Pre-Pregnancy BMI max
11	Mother Pre-Pregnancy BMI median
12	Mother 24-40 weeks MCV
13	Mother Pre-Pregnancy BMI mean
14	Mother 24-40 weeks MCH
15	Age of Father at birth
16	Siblings count BMI zscore std
17	Mother Pre-Pregnancy BMI min
18	Mother last BMI 24-40 weeks
19	Age of Mother at birth
20	Father Weight median
21	Mother Pre-Pregnancy Weight max
22	Mother last BMI 0-12 weeks
23	Father Height median
24	Mother Pre-Pregnancy Weight mean
25	Mother 12-24 weeks VITAMIN B12
26	Mother 0-12 weeks MCH
27	Father std Cholesterol
28	Mother Pre-Pregnancy Weight median
29	Siblings min BMI zscore std
30	Mother Pre-Pregnancy Weight min
31	Mother Pre-pregnancy CMV IgG
32	Mother Pre-pregnancy PDW
33	Mother 24-40 weeks GLUCOSE 50 g
34	Mother Pre-pregnancy GGT
35	Father Height mean
36	Siblings mean BMI zscore std
37	Father max Triglycerides
38	Mother 12-24 weeks RBC
39	Mother 0-12 weeks WBC
40	Siblings std BMI zscore mean
41	Mother last Diastolic Blood Pressure 24-40 weeks
42	Mother 12-24 weeks HB
43	Mother 12-24 weeks LUC %
44	Mother 0-12 weeks VITAMIN B12
45	Mother 0-12 weeks HCT
46	Mother Pre-pregnancy GLUCOSE 50 g
47	Father mean Cholesterol- hdl
48	Father mean Triglycerides
49	Father Height min
50	Siblings count BMI zscore mean
51	Mother 0-12 weeks LYMP.abs
52	Mother Pre-pregnancy GLUCOSE
53	Mother last BMI 12-24 weeks
54	Father std Glucose
55	Mother Pre-pregnancy CK—CREAT.KINASE(CPK)
56	Father std Cholesterol-ldl calc
57	Father min Cholesterol- hdl
58	Mother last BMI Pre-pregnancy
59	Mother Pre-pregnancy TSH
60	Mother last Weight Pre-pregnancy
61	Mother Pre-pregnancy MCHC
62	Mother Pre-pregnancy LYMP.abs
63	Siblings median BMI zscore std
64	Mother 12-24 weeks IRON
65	Mother count Roxithromycin
66	Mother last Weight 12-24 weeks
67	Mother 24-40 weeks MPV
68	Mother 12-24 weeks GLUCOSE
69	Mother Pre-pregnancy PT %
70	Mother 24-40 weeks VITAMIN B12
71	Father max Glucose
72	Father Weight max
73	Mother 24-40 weeks EOS %
74	Mother 24-40 weeks GLUCOSE (GTT) 0′
75	Mother Pre-pregnancy HCT
76	Mother Pre-pregnancy BILIRUBIN-DIRECT
77	Mother 0-12 weeks MPV
78	Siblings max BMI zscore std
79	Father mean Glucose
80	Mother 0-12 weeks NEUT.abs
81	Father Weight mean
82	Mother Pre-pregnancy T4- FREE
83	Mother 24-40 weeks RBC
84	Mother Pre-pregnancy LYM %
85	Mother 24-40 weeks ALK. PHOSPHATASE
86	Mother 0-12 weeks EOS.abs
87	Father min Triglycerides
88	Mother 0-12 weeks MONO.abs
89	Mother Pre-pregnancy MPV
90	Mother Pre-pregnancy NEUT %
91	Mother 24-40 weeks APTT-R
92	Siblings at 13 years of age BMI zscore mean
93	Mother Pre-pregnancy PHOSPHORUS
94	Father count Metformin
95	Mother Pre-pregnancy NEUT.abs
96	Mother 12-24 weeks MCHC
97	Mother 24-40 weeks HEMOGLOBIN A1C %
98	Mother Pre-pregnancy CHOLESTEROL-LDL calc
99	Mother last Systolic Blood Pressure 0-12 weeks
100	Father median Triglycerides
101	Mother 24-40 weeks MICRO %
102	Mother last Systolic Blood Pressure 12-24 weeks
103	Mother 24-40 weeks MONO.abs
104	Mother 12-24 weeks PLT
105	Mother Pre-pregnancy LDH
106	Mother 0-12 weeks HEPATITIS Bs Ab
107	Mother Pre-pregnancy PLT
108	Father min Glucose
109	Father max Non-hdl_cholesterol
110	Mother 12-24 weeks NEUT %
111	Mother 24-40 weeks HYPO %
112	Mother last Systolic Blood Pressure Pre-pregnancy
113	Father Height max
114	Mother last Systolic Blood Pressure 24-40 weeks
115	Father median Cholesterol- hdl
116	Mother 12-24 weeks T4- FREE
117	Mother Pre-pregnancy UREA
118	Mother Pre-pregnancy MAGNESIUM
119	Mother 0-12 weeks CHOLESTEROL/HDL
120	Mother 24-40 weeks LYM %
121	Mother 12-24 weeks MCV
122	Mother Pre-pregnancy MONO.abs
123	Mother Pre-pregnancy WBC
124	Mother 12-24 weeks MONO.abs
125	Mother 24-40 weeks HCT
126	Mother 0-12 weeks CMV IgG
127	Mother 24-40 weeks PLT
128	Mother Pre-pregnancy PROTEIN-TOTAL
129	Mother 12-24 weeks CMV IgG
130	Mother 24-40 weeks CMV IgG
131	Mother 0-12 weeks SODIUM
132	Mother 24-40 weeks NEUT %
133	Mother 24-40 weeks MCHC
134	Father Weight min
135	Mother count Amoxicillin
136	Father mean Cholesterol
137	Father median Glucose
138	Mother Pre-pregnancy CHOLESTEROL
139	Mother 0-12 weeks MONO %
140	Mother 24-40 weeks LYMP.abs
141	Mother 12-24 weeks NEUT.abs
142	Mother Pre-pregnancy HYPER %
143	Mother 12-24 weeks TSH
144	Mother count Cabergoline
145	Mother last Weight 0-12 weeks
146	Mother Pre-pregnancy PCT
147	Father Height std
148	Mother 0-12 weeks TRIGLYCERIDES
149	Mother 0-12 weeks GLUCOSE
150	Father std Cholesterol/hdl
151	Mother Pre-pregnancy HYPO %
152	Mother 24-40 weeks FERRITIN
153	Father BMI std
154	Mother Pre-pregnancy BASO %
155	Mother 24-40 weeks SODIUM
156	Mother Pre-pregnancy VITAMIN B12
157	Mother 0-12 weeks ESTRADIOL (E-2)
158	Mother 0-12 weeks LYM %
159	Mother 12-24 weeks EOS %
160	Mother 24-40 weeks NEUT.abs
161	Mother 24-40 weeks NEUTROPHILS abs-DIF
162	Father diagnosed Diabetes mellitus
163	Mother Pre-pregnancy CREATININE
164	Father Weight std
165	Mother 24-40 weeks HB
166	Mother BMI delta 12-24 weeks to 24-40 weeks
167	Mother 0-12 weeks GGT
168	Mother 0-12 weeks LH
169	Mother 24-40 weeks RDW
170	Mother 12-24 weeks HbA2
171	Mother 0-12 weeks MCV
172	Mother Pre-pregnancy MONO %
173	Mother Pre-pregnancy HB
174	Mother 24-40 weeks LUC %
175	Mother count Enoxaparin
176	Mother 24-40 weeks MONO %
177	Mother 0-12 weeks NEUT %
178	Mother 24-40 weeks WBC
179	Father mean Non-hdl_cholesterol
180	Mother 0-12 weeks EOS %
181	Mother 0-12 weeks RDW
182	Mother Pre-pregnancy RDW
183	Mother 12-24 weeks LYM %
184	Mother Pre-pregnancy SHBG
185	Mother Pre-pregnancy FOLIC ACID
186	Mother 0-12 weeks HYPO %
187	Mother Pre-pregnancy MICRO %
188	Mother 24-40 weeks BILIRUBIN TOTAL
189	Mother Pre-pregnancy SODIUM
190	Mother Pre-pregnancy RBC
191	Mother 24-40 weeks BASO %
192	Mother 24-40 weeks LYMPHOCYTES abs-DIF
193	Mother 0-12 weeks PROGESTERONE
194	Father BMI count
195	Mother Pre-pregnancy TRIGLYCERIDES
196	Father max Cholesterol
197	Mother 12-24 weeks LYMP.abs
198	Mother last Diastolic Blood Pressure 0-12 weeks
199	Mother Pre-pregnancy GLOBULIN
200	Mother 24-40 weeks CREATININE
201	Father max Cholesterol-ldl calc
202	Father max Cholesterol- hdl
203	Mother Pre-pregnancy ESR
204	Mother 12-24 weeks PT-SEC
205	Mother 24-40 weeks LUC abs
206	Mother 24-40 weeks MPXI
207	Mother Pre-Pregnancy BMI std
208	Mother 12-24 weeks FERRITIN
209	Mother 0-12 weeks MPXI
210	Mother 0-12 weeks TSH
211	Mother 24-40 weeks GOT (AST)
212	Mother 24-40 weeks HYPER %
213	Mother 24-40 weeks EOSINOPHILS abs-DIF
214	Mother 12-24 weeks WBC
215	Father mean Cholesterol-ldl calc
216	Father std Triglycerides
217	Mother Pre-pregnancy HDW
218	Mother 0-12 weeks UREA
219	Mother 12-24 weeks HCT
220	Mother Pre-pregnancy HEPATITIS Bs Ab
221	Mother 0-12 weeks LDH
222	Mother 12-24 weeks POTASSIUM
223	Mother Pre-Pregnancy Weight std
224	Mother 12-24 weeks MICRO %
225	Mother Pre-pregnancy BILIRUBIN TOTAL
226	Mother 0-12 weeks HB
227	Mother Pre-pregnancy C-REACTIVE PROTEIN
228	Mother Pre-pregnancy MCV
229	Mother Pre-pregnancy DHEA SULPHATE
230	Father min Cholesterol
231	Mother Pre-pregnancy EOS %
232	Father median Cholesterol
233	Mother 24-40 weeks BILIRUBIN-DIRECT
234	Mother 24-40 weeks STABS %-DIF
235	Siblings at 5 years of age BMI zscore std
236	Father std Cholesterol- hdl
237	Mother 12-24 weeks HYPO %
238	Mother 24-40 weeks STABS abs-DIF
239	Mother 0-12 weeks LUC %
240	Mother 12-24 weeks SODIUM
241	Mother 24-40 weeks GLUCOSE (GTT) 60′
242	Mother 24-40 weeks CHOLESTEROL
243	No. of Siblings with BMI data
244	Mother 12-24 weeks CREATININE
245	Mother 24-40 weeks GLUCOSE (GTT) 180′
246	Mother 12-24 weeks EOS.abs
247	Mother Pre-pregnancy COMPLEMENT C3
248	Mother Pre-pregnancy EOS.abs
249	Mother 24-40 weeks T3- FREE
250	Mother Pre-pregnancy FERRITIN
251	Mother Pre-pregnancy AMYLASE
252	Father count Pravastatin
253	Mother 24-40 weeks MONOCYTES abs-DIF
254	Mother 24-40 weeks GPT (ALT)
255	Mother Pre-pregnancy URIC ACID
256	Father diagnosed Obesity, unspecified
257	Mother 24-40 weeks NEUTROPHILS %-DIF
258	Mother 0-12 weeks MCHC
259	Mother 12-24 weeks MONO %
260	Mother Pre-pregnancy FIBRINOGEN CALCU
261	Mother Pre-pregnancy MPXI
262	Mother 0-12 weeks URIC ACID
263	Mother Pre-pregnancy LH
264	Mother 24-40 weeks MACRO %
265	Mother Pre-pregnancy MCH
266	Mother 24-40 weeks BASO abs
267	Father count Cholesterol-ldl calc
268	Mother 0-12 weeks MICRO %
269	Mother Weight delta Pre-pregnancy to 0-12 weeks
270	Siblings std BMI zscore std
271	Mother 24-40 weeks LDH
272	Mother 0-12 weeks PLT
273	Siblings at 13 years of age BMI zscore std
274	Father count Glucose
275	Mother Pre-pregnancy BILIRUBIN INDIRECT
276	Mother 24-40 weeks URIC ACID
277	Mother BMI delta Pre-pregnancy to 0-12 weeks
278	Mother 12-24 weeks GGT
279	Mother 0-12 weeks GPT (ALT)
280	Mother 0-12 weeks PHOSPHORUS
281	Mother Pre-pregnancy LUC %
282	Mother 0-12 weeks HYPER %
283	Mother 0-12 weeks CREATININE
284	Mother 12-24 weeks MICRO %/HYPO %
285	Mother 0-12 weeks MACRO %
286	Mother 12-24 weeks RDW
287	Mother Pre-pregnancy POTASSIUM
288	Mother 0-12 weeks RBC
289	Mother Pre-pregnancy ALK. PHOSPHATASE
290	Mother Pre-pregnancy ALBUMIN
291	Mother 12-24 weeks TRIGLYCERIDES
292	Mother 0-12 weeks AMYLASE
293	Father min Cholesterol-ldl calc
294	Mother 0-12 weeks ALK. PHOSPHATASE
295	Mother Pre-pregnancy PT-SEC
296	Mother 0-12 weeks VITAMIN D (25-OH)
297	Mother 12-24 weeks MCH
298	Mother Pre-pregnancy CALCIUM
299	Father count Cholesterol- hdl
300	Father median Cholesterol-ldl calc
301	Mother Pre-pregnancy COMPLEMENT C4
302	Mother count Ofloxacin
303	Mother last Weight 24-40 weeks
304	Mother 0-12 weeks CHOLESTEROL-LDL calc
305	Mother Pre-pregnancy MACRO %
306	Mother count Phenoxymethylpenicillin
307	Mother 0-12 weeks HDW
308	Mother 24-40 weeks TRIGLYCERIDES
309	Mother Pre-pregnancy TESTOSTERONE- TOTAL
310	Father std Non-hdl_cholesterol
311	Mother 0-12 weeks NON-HDL_CHOLESTEROL
312	Mother last Diastolic Blood Pressure Pre-pregnancy
313	Mother 0-12 weeks APTT-sec
314	Mother 24-40 weeks MICRO %/HYPO %
315	Mother 12-24 weeks MPXI
316	Mother 0-12 weeks BASO %
317	Father min Non-hdl_cholesterol
318	Mother Pre-pregnancy NON-HDL_CHOLESTEROL
319	Mother 0-12 weeks GLOBULIN
320	Mother 12-24 weeks MACRO %
321	Father count Simvastatin
322	Mother 12-24 weeks LUC abs
323	Mother 12-24 weeks PT-INR
324	Mother 0-12 weeks GOT (AST)
325	Father min Cholesterol/hdl
326	Mother 24-40 weeks GLUCOSE
327	Mother 24-40 weeks EOS.abs
328	Mother 12-24 weeks UREA
329	Mother 0-12 weeks PROTEIN-TOTAL
330	Mother Pre-pregnancy ALY
331	Mother Pre-pregnancy FREE ANDROGEN INDEX
332	Mother 0-12 weeks POTASSIUM
333	Mother 12-24 weeks AMYLASE
334	Mother 12-24 weeks CK—CREAT.KINASE(CPK)
335	Mother Pre-pregnancy GPT (ALT)
336	Mother 0-12 weeks CHOLESTEROL
337	Mother 12-24 weeks BASO %
338	Mother Pre-pregnancy CORTISOL-BLOOD
339	Mother 24-40 weeks RDW-CV
340	Mother Pre-pregnancy ESTRADIOL (E-2)
341	Mother 12-24 weeks MPV
342	Mother Pre-pregnancy PROLACTIN
343	Mother 24-40 weeks TSH
344	is Male
345	Mother 0-12 weeks CK—CREAT.KINASE(CPK)
346	Father median Non-hdl_cholesterol
347	Father mean Cholesterol/hdl
348	Mother 0-12 weeks FOLIC ACID
349	Mother 24-40 weeks IRON
350	Mother 0-12 weeks LUC abs
351	Mother Pre-pregnancy RUBELLA Ab IgG
352	Mother 0-12 weeks ALBUMIN
353	Mother 0-12 weeks IRON
354	Mother 0-12 weeks RUBELLA Ab IgG
355	Mother 24-40 weeks AMYLASE
356	Number of twin siblings
357	Mother Pre-pregnancy ANDROSTENEDIONE
358	Father count Enalapril
359	Mother count Mebendazole
360	Mother 24-40 weeks CHLORIDE
361	Mother 24-40 weeks HDW
362	Mother 24-40 weeks GLUCOSE (GTT) 120′
363	Father count Cholesterol
364	Mother 12-24 weeks PCT
365	Mother 24-40 weeks UREA
366	Mother 0-12 weeks TOXOPLASMA IgG
367	Mother Pre-pregnancy MICRO %/HYPO %
368	Mother 24-40 weeks PROTEIN-TOTAL
369	Mother 12-24 weeks TOXOPLASMA IgG
370	Mother 0-12 weeks FSH
371	Father count Non-hdl_cholesterol
372	Mother 24-40 weeks CHOLESTEROL- HDL
373	Mother 24-40 weeks PT-SEC
374	Mother Pre-pregnancy ANTI CARDIOLIPIN IgG
375	Mother Pre-Pregnancy BMI count
376	Mother 24-40 weeks PDW
377	Mother 24-40 weeks MONOCYTES %-DIF
378	Mother 0-12 weeks MICRO %/HYPO %
379	Mother Pre-pregnancy TRANSFERRIN
380	Mother Pre-pregnancy GOT (AST)
381	Mother Pre-pregnancy PT-INR
382	Mother 24-40 weeks CALCIUM
383	Mother 0-12 weeks HEMOGLOBIN A
384	Mother Pre-pregnancy LUC abs
385	Father count Amlodipine
386	Mother 12-24 weeks ALK. PHOSPHATASE
387	Father count Triglycerides
388	Mother 0-12 weeks CALCIUM
389	Mother 12-24 weeks FOLIC ACID
390	Mother Pre-pregnancy FSH
391	Mother Pre-pregnancy CHOLESTEROL- HDL
392	Mother Pre-pregnancy PROGESTERONE
393	Mother 0-12 weeks T4- FREE
394	Mother 12-24 weeks BASO abs
395	Mother Pre-pregnancy ANTITHROMBIN-III
396	Mother 24-40 weeks TOXOPLASMA IgG
397	Mother 0-12 weeks PT-SEC
398	Mother Pre-pregnancy CONTROL PTT
399	Mother 24-40 weeks EOSINOPHILS %-DIF
400	Mother Pre-pregnancy 17-OH-PROGESTERONE
401	Father count Cholesterol/hdl
402	Mother Pre-pregnancy IRON
403	Mother Pre-pregnancy HEMOGLOBIN A1C %
404	Mother 12-24 weeks HYPER %
405	Mother 0-12 weeks BASO abs
406	Mother Pre-pregnancy APTT-sec
407	Mother count Fluticasone
408	Mother 24-40 weeks HCT/HGB Ratio
409	Father count Bezafibrate
410	Father diagnosed Obesity (bmi >30)
411	Mother count Omeprazole
412	Mother 24-40 weeks PT-INR
413	Mother Pre-pregnancy HCT/HGB Ratio
414	Mother Pre-Pregnancy Weight count
415	Mother count Estradiol
416	Mother 24-40 weeks PCT
417	Mother Pre-pregnancy T3-TOTAL
418	Mother count Follitropin alfa
419	Mother 24-40 weeks PT %
420	Mother Pre-pregnancy VLDL
421	Mother 24-40 weeks POTASSIUM
422	Mother 12-24 weeks HbF
423	Mother 24-40 weeks BILIRUBIN INDIRECT
424	Mother 24-40 weeks GLOM.FILTR.RATE
425	Mother 24-40 weeks PHOSPHORUS
426	Father max Cholesterol/hdl
427	Mother Pre-pregnancy ALY %
428	Mother 0-12 weeks PT %
429	Mother 12-24 weeks PT %
430	Mother 24-40 weeks TRANSFERRIN
431	Father Weight count
432	Mother Pre-pregnancy T3- FREE
433	Mother 12-24 weeks PROTEIN-TOTAL
434	Mother Pre-pregnancy BASO abs
435	Mother 0-12 weeks T3- FREE
436	Mother Pre-pregnancy RDW-CV
437	Mother count Levothyroxine sodium
438	Mother 12-24 weeks ALBUMIN
439	Mother 12-24 weeks CHOLESTEROL
440	Mother 24-40 weeks MAGNESIUM
441	Mother 0-12 weeks PDW
442	Mother 0-12 weeks TRANSFERRIN
443	Mother 24-40 weeks HbA2
444	Mother 12-24 weeks T3- FREE
445	Mother count Aspirin
446	Mother 0-12 weeks BLOOD TYPE
447	Mother count Human menopausal gonadotrophin
448	Mother count Co-amoxiclav cd
449	Mother 24-40 weeks T4- FREE
450	Mother 0-12 weeks DHEA SULPHATE
451	Mother 0-12 weeks PROLACTIN
452	Mother 24-40 weeks LYMPHOCYTES %-DIF
453	Mother 0-12 weeks FERRITIN
454	Mother count Symbicort/duoresp
455	Mother Pre-pregnancy PROTEIN C ACTIVITY
456	Mother 0-12 weeks HCT/HGB Ratio
457	Mother Pre-pregnancy CHOLESTEROL/HDL
458	Mother 12-24 weeks NORMOBLAST.abs
459	Father median Cholesterol/hdl
460	Mother 24-40 weeks ALBUMIN
461	Mother last Diastolic Blood Pressure 12-24 weeks
462	Mother 0-12 weeks RDW-CV
463	Mother 12-24 weeks URIC ACID
464	Apidoral given at birth
465	Mother 12-24 weeks BILIRUBIN TOTAL
466	Mother 0-12 weeks BILIRUBIN TOTAL
467	Father diagnosed Other and unspecified hyperlipidemia
468	Mother Pre-pregnancy ANTI CARDIOLIPIN IgM
469	Mother 24-40 weeks APTT-sec
470	Mother 24-40 weeks VITAMIN D (25-OH)
471	Mother 24-40 weeks GLOBULIN
472	Mother Pre-pregnancy CA-125
473	Mother count Cetirizine
474	Mother 12-24 weeks APTT-sec
475	Mother 12-24 weeks LDH
476	Mother 24-40 weeks CK—CREAT.KINASE(CPK)
477	Mother 0-12 weeks BILIRUBIN-DIRECT
478	Mother 12-24 weeks GPT (ALT)
479	Mother Pre-pregnancy APTT-R
480	Mother 24-40 weeks FIBRINOGEN CALCU
481	Mother 12-24 weeks NORMOBLAST.%
482	Mother 0-12 weeks CHOLESTEROL- HDL
483	Mother count Desogestrel
484	Mother Pre-pregnancy EOSINOPHILS %-DIF
485	Mother 24-40 weeks FOLIC ACID
486	Mother Pre-pregnancy IgA
487	Mother Pre-pregnancy PROT-S ANTIGEN (FREE
488	Mother count Lansoprazole
489	Mother 12-24 weeks CHOLESTEROL-LDL calc
490	Mother 12-24 weeks ALPHA FETOPROTEIN TM
491	Mother 12-24 weeks GLUCOSE 50 g
492	Mother 0-12 weeks HbF
493	Mother BMI delta 0-12 weeks to 12-24 weeks
494	Mother 0-12 weeks BILIRUBIN INDIRECT
495	Mother Weight delta 12-24 weeks to 24-40 weeks
496	Mother count Cefuroxime
497	Mother 12-24 weeks CALCIUM
498	Father diagnosed Essential hypertension
499	Mother Pre-pregnancy MONOCYTES abs-DIF
500	Mother 12-24 weeks RDW-CV
501	Mother 0-12 weeks PCT
502	Mother Pre-pregnancy LYMPHOCYTES %-DIF
503	Mother 24-40 weeks NORMOBLAST.abs
504	Mother Pre-pregnancy FIBRINOGEN
505	Mother count Cefalexin
506	Mother Pre-pregnancy CHLORIDE
507	Mother count Progesterone
508	Mother 12-24 weeks GOT (AST)
509	Mother 12-24 weeks PDW
510	Father diagnosed Morbid obesity
511	Mother Pre-pregnancy BLOOD TYPE
512	Mother 0-12 weeks HbA2
513	Mother Weight delta 0-12 weeks to 12-24 weeks
514	Mother 24-40 weeks NON-HDL_CHOLESTEROL
515	Mother 12-24 weeks HDW
516	Mother Pre-pregnancy GLOM.FILTR.RATE
517	Premature birth
518	Mother Pre-pregnancy VITAMIN D (25-OH)
519	Mother 24-40 weeks CHOLESTEROL-LDL calc
520	Mother 12-24 weeks CHLORIDE
521	Born in Israel
522	Mother 12-24 weeks CHOLESTEROL- HDL
523	Mother Pre-pregnancy HbA2
524	Mother 0-12 weeks CHLORIDE
525	Mother Pre-pregnancy LIC
526	Mother 24-40 weeks NORMOBLAST.%
527	Mother 0-12 weeks NORMOBLAST.%
528	Mother Pre-pregnancy NORMOBLAST.%
529	Mother Pre-pregnancy LIC %
530	Mother 24-40 weeks LI
531	Mother Pre-pregnancy NEUTROPHILS abs-DIF
532	Mother Pre-pregnancy TOXOPLASMA IgG
533	Mother 24-40 weeks CONTROL PTT
534	Mother 12-24 weeks NON-HDL_CHOLESTEROL
535	Mother Pre-pregnancy HbF
536	Mother Pre-pregnancy NEUTROPHILS %-DIF
537	Father Height count
538	Mother Pre-pregnancy MONOCYTES %-DIF
539	Mother Pre-pregnancy LYMPHOCYTES abs-DIF
540	Mother 12-24 weeks PHOSPHORUS
541	Mother 12-24 weeks HbA
542	Mother Pre-pregnancy HEMOGLOBIN A
543	Mother 24-40 weeks GGT
544	Mother 12-24 weeks BILIRUBIN-DIRECT
545	Mother 0-12 weeks HbA
546	Mother 24-40 weeks FIBRINOGEN
547	Mother 0-12 weeks NORMOBLAST.abs
548	Mother count Carbamazepine
549	Mother count Norgestimate and ethinylestradiol
550	Mother count Norethisterone
551	Mother count Nitrofurantoin
552	Mother count Metronidazole
553	Mother count Methylphenidate
554	Mother count Medroxyprogesterone
555	Mother count Loratadine
556	Mother count Ipratropium bromide
557	Mother count Gestodene and ethinylestradiol
558	Mother count Follitropin beta
559	Mother count Fluoxetine
560	Mother count Fluconazole
561	Mother count Fexofenadine
562	Mother count Famotidine
563	Mother count Escitalopram
564	Mother 0-12 weeks PT-INR
565	Mother count Dydrogesterone
566	Mother count Drospirenone and ethinylestradiol
567	Mother count Doxycycline
568	Mother count Dexamethasone
569	Mother count Desogestrel and ethinylestradiol
570	Mother count Desloratadine
571	Mother count Colchicine
572	Mother count Clonazepam
573	Mother count Clomifene
574	Mother count Clarithromycin
575	Mother count Citalopram
576	Mother count Ciprofloxacin
577	Mother count Chorionic gonadotrophin
578	Mother count Paroxetine
579	Mother count Prednisone
580	Mother 12-24 weeks TRANSFERRIN
581	Mother count Progyluton cd
582	Mother count Triptorelin
583	Mother count Simvastatin
584	Mother count Sertraline
585	Mother count Seretide cd
586	Mother count Salbutamol
587	Mother count Choriogonadotropin alfa
588	Mother count Budesonide
589	Mother 12-24 weeks GLOBULIN
590	Mother 12-24 weeks BILIRUBIN INDIRECT
591	Father count Rosuvastatin
592	Father count Ramipril-hydrochlorothiazide cd
593	Father count Ramipril
594	Father count Propranolol
595	Father count Nifedipine-cd
596	Father count Nifedipine
597	Father count Metformin and sitagliptin cd
598	Mother 0-12 weeks GLOM.FILTR.RATE
599	Father count Insulin glargine
600	Father count Bisoprolol
601	Father count Atorvastatin
602	Father count Atenolol
603	Mother 12-24 weeks RUBELLA Ab IgG
604	Mother Pre-pregnancy NORMOBLAST.abs
605	Mother 0-12 weeks ESR
606	Mother count Bethamethasone
607	Mother count Anti-d (rh) immunoglobulin
608	Mother count Aciclovir
609	Mother 12-24 weeks BLOOD TYPE
610	Mother 24-40 weeks BLOOD TYPE
611	Mother 12-24 weeks HCT/HGB Ratio
612	Father diagnosed Unspecified essential hypertension
613	Father diagnosed Overweight (bmi <30)
614	Father diagnosed Other abnormal glucose
615	Father diagnosed Lipid metabolism disorder
616	Father diagnosed Impaired fasting glucose
617	Father diagnosed Disorders of lipoid metabolism
618	Father diagnosed Diabetes mellitus without mention of
	complication
619	Father diagnosed Adult-onset type diabetes mellitus whithout
	complication
620	Mother count Lamotrigine

Table 1.3 presents a list of 66 response parameters from which parameter to be included in questionnaire can be selected when the subject is an infant or toddler subject. The questionnaire can presented to a person on behalf of the subject, and can provide response parameters for feeing the machine learning procedure. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.3, than a parameter that is listed lower in Table 1.3. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.3, where N≤M≤66.

TABLE 1.3
No.	Parameter

1	Last WFL zscore
2	Siblings mean BMI zscore mean
3	Father BMI mean
4	Weight Routine checkup - 18-22 months
5	Weight Routine checkup - 12-16 months
6	Weight Routine checkup - 4-6 months
7	Ethnicity: North Africa
8	Weight Routine checkup - 9-12 months
9	WFL Routine checkup - 18-22 months
10	WFL Routine checkup - 12-16 months
11	WFL Routine checkup - 1-2 months
12	Mother last BMI Pre-pregnancy
13	Date of Birth
14	WFL Routine checkup - 9-12 months
15	Age of Father at birth
16	Siblings mean BMI zscore std
17	Age of Mother at birth
18	Ethnicity: West Europe
19	Weight Routine checkup - 6-9 months
20	WFL Routine checkup - 4-6 months
21	Father Weight mean
22	WFL Routine checkup - 2-3 months
23	Mother last BMI 0-12 weeks
24	Mother last Weight Pre-pregnancy
25	Ethnicity: North America
26	Mother last BMI 24-40 weeks
27	No. of Siblings with BMI data
28	Weight Routine checkup - 2-3 months
29	Ethnicity: Unknown
30	WFL Routine checkup - 6-9 months
31	Height Routine checkup - 12-16 months
32	Ethnicity: Ethiopia
33	Height Routine checkup - 18-22 months
34	Ethnicity: East Europe
35	Week of year bom
36	Birth weight
37	Mother last BMI 12-24 weeks
38	Weight Routine checkup - 1-2 months
39	Height Routine checkup - 9-12 months
40	Age at last WFL
41	Age at Target measurement
42	Mother last Weight 12-24 weeks
43	Height Routine checkup - 2-3 months
44	Height Routine checkup - 6-9 months
45	Ethnicity: Iraq
46	Ethnicity: Muslim Arab
47	Height Routine checkup - 4-6 months
48	Mother BMI delta 12-24 weeks to 24-40 weeks
49	Height Routine checkup - 1-2 months
50	Mother last Weight 0-12 weeks
51	Ethnicity: Iran
52	Mother BMI delta Pre-pregnancy to 0-12 weeks
53	Mother last Weight 24-40 weeks
54	Mother Weight delta Pre-pregnancy to 0-12 weeks
55	Ethnicity: Asian
56	Ethnicity: Yemen
57	is Male
58	Mother Weight delta 0-12 weeks to 12-24 weeks
59	Ethnicity: USSR
60	Ethnicity: Mediterranean
61	Mother Weight delta 12-24 weeks to 24-40 weeks
62	Mother BMI delta 0-12 weeks to 12-24 weeks
63	Ethnicity: Latin America
64	Born in Israel
65	Premature birth
66	Ethnicity: Africa

Table 1.4 presents a list of 21 response parameters from which parameter to be included in questionnaire can be selected when the subject is an unborn subject. The questionnaire can presented to a person on behalf of the subject, and can provide response parameters for feeing the machine learning procedure. The list is sorted according the significance of the respective feature for predicting the likelihood for childhood obesity, in descending order, so that from the standpoint of prediction accuracy it is more preferred to select a parameter that is listed higher in Table 1.4, than a parameter that is listed lower in Table 1.4. For example, when N parameters are used, it is preferred to select those parameters from lines 1 through M of Table 1.4, where N≤M≤21.

TABLE 1.4
No.	Parameter

1	Siblings mean BMI zscore mean
2	Father BMI mean
3	Mother last BMI Pre-pregnancy
4	Age of Father at birth
5	Siblings mean BMI zscore std
6	Age of Mother at birth
7	Father Weight mean
8	Mother last BMI 0-12 weeks
9	Mother last Weight Pre-pregnancy
10	Mother last BMI 24-40 weeks
11	No. of Siblings with BMI data
12	Mother last BMI 12-24 weeks
13	Mother last Weight 12-24 weeks
14	Mother BMI delta 12-24 weeks to 24-40 weeks
15	Mother last Weight 0-12 weeks
16	Mother BMI delta Pre-pregnancy to 0-12 weeks
17	Mother last Weight 24-40 weeks
18	Mother Weight delta Pre-pregnancy to 0-12 weeks
19	Mother Weight delta 0-12 weeks to 12-24 weeks
20	Mother Weight delta 12-24 weeks to 24-40 weeks
21	Mother BMI delta 0-12 weeks to 12-24 weeks

Example 2

This Example describes analysis of data collected over a decade from Israel's largest healthcare provider, to assess risk factors for pediatric obesity and to develop a model for assessing children's obesity risk in order to inform and target interventions. The inventors analyzed nationwide electronic health records of children from 2006 to 2018 for whom sequential anthropometric data were available. Obesity was defined as body mass index (BMI)≥95th percentile for age and gender. Data of children and their families included anthropometric measurements, drug prescriptions, medical diagnoses, demographic data and laboratory tests.

Analysis of BMI trajectories among 382,132 adolescents revealed that among obese adolescents, the largest annual increase in BMI percentile occurs at 2-5 years of age. Therefore, the inventors devised a computational model based on data of 136,196 children from birth up to 2 years of age for predicting obesity at 5-6 years of age and from birth and up to 2 years of age. Most (51%) obese children in our cohort had a normal weight at infancy. As will be shown below, the model predicted obesity with an area under the receiver operating characteristic curve (auROC) and 95% CI of 0.803 [0.796−0.812]. Discrimination results on different subpopulations demonstrated its robustness across a clinically heterogeneous pediatric population. The most influential features included anthropometric measurements of the child and the family. Other impactful features included ethnicity and maternal pregnancy glucose measurements. A model based solely on features that are available pre-birth had similar performance to a model based on the child's last available weight and length measurements.

Methods

Study Design and Population

Extracted features included maternal, paternal and siblings' data. FIG. 3 illustrates the dataset used in the present Example. The dataset contained 1,449,442 children who have at least one measurement in a routine medical infant checkup which is scheduled for all Israeli infants at ages 1, 2, 4, 6, 9, 12, and 18 months. Of them, 643,463 children have an additional measurement between 5 and 6 years of age, which was defined as the outcome for the machine learning procedure. 136,196 children who have at least 2 different routine checkup measurements in addition to the 5-6 years old outcome measurement were included in the cohort. 90,270 children included in the cohort have maternal data, 92,152 have paternal data and 70,735 have data of at least one sibling.

Features

All EHR data available were binned into time periods and statistical measures (e.g., median, max, slope) were taken as features for each period. Pharmaceutical prescriptions and clinical diagnoses were categorized by ATC codes (Anon n.d.) and ICD9 diagnosis codes, respectively, and counts in different time periods were taken as features. Weight, height, Weight-for-Length (WFL) and BMI data were converted to reference z-scores provided by the Center for Disease Control and Prevention (CDC) (Barlow and Expert Committee 2007). Valid measurements were defined as being in the range of 5 CDC standard deviation scores for weight and height. Features from maternal pregnancy were binned in alignment with the routine pregnancy tests schedule in Israel. Specific features of interest such as antibiotic prescriptions, ethnicity, and socioeconomic status surrogates were devised manually based on domain knowledge. Altogether, 943 features were devised for each child.

The characteristics of the Study Cohort and features used are summarized in Table 2.1, below.

	TABLE 2.1
	Train set	Temporal test set
	(n = 108,416)	(n = 27,780)
	aged 5 before 2017	aged 5 at 2017	All

Children (n = 136,196)

Obesity status at 5-6 years	Underweight	13,635	3,304	16,939
of age	Normal weight	75,648	19,867	95,515
	Overweight	19,133	4,609	23,742
	Obese	8,120	1,941	10,061
Sex	Female	52,733	13,458	66,191
	Male	55,683	14,322	70,005

Children with maternal data (n = 90,270)

Maternal age at childbirth	mean (std)	30.1 (5.2)	30.5 (5.2)	30.1 (5.2)
[years]
Pre-pregnancy BMI	mean (std)	23.6 (4.7)	23.3 (4.4)	23.5 (4.6)
[m/kg2]

Children with paternal data (n = 92,152)

Paternal age [years]	mean (std)	33.1 (5.9)	33.3 (5.7)	33.2 (5.9)
Paternal BMI [m/kg2]	mean (std)	25.9 (4.4)	25.6 (4.2)	25.9 (4.3)

Children with Siblings data (n = 70,735)

Number of children with	count	55070	15665	70735
siblings data
Number of siblings per	mean (std)	1.1 (1.3)	1.3 (1.4)	1.2 (1.3)
child
Sibling BMI CDC z-score	mean (std)	0.0 (1.1)	−0.1 (1.1)	0.0 (1.1)

Outcome

The outcome for the models was the obesity status of children at 5 to 6 years of age. Obesity status was defined in accordance with health care professionals in Israel, using the CDC BMI reference percentiles. Cutoffs for normal weight, overweight, and obesity were determined using the CDC's standard thresholds of the 85th percentile for overweight and 95th percentile for obesity. Using other percentiles curves such as, but not limited to, the World Health Organization (WHO) WFL, and WHO BMI provided similar estimates of obesity risk as the CDC percentiles at 5 years of age.

Statistical Analysis

Childhood Obesity Prediction Model

In this Example, Gradient Boosting trees were trained for providing the prediction. Trees allow nonlinear and multiple feature interactions to be captured, which may be important in obtaining an accurate prediction model. The parameters of the model were tuned using cross-validation on the training set. As stringent tests, both temporal and geographical validations were used, thus testing the performance of the model for distribution shifts over time and geographic location. The temporal validation set contained the most recent year in which the data were available. The geographical validation set contained all the clinics in the most populated and multiethnic city in Israel, Jerusalem. Unless stated otherwise, the reported results are on the temporal validation sets. Full results on both validation sets are available in Table 2.2, below.

As a baseline model for comparison the last WFL percentile routine checkup measurement available before 2 years of age was used, as current guidelines recommend that clinicians assess a child's current nutritional and obesity status by calculating WFL percentile or BMI percentile in children 0 to 2 years of age, or older than 2 years of age, respectively (Daniels et al. 2015). The WFL percentile thus emulates the information a caregiver has today to assess the current obesity status and future obesity risk of children younger than 2 years of age (Taveras et al. 2009). This variable also contains information of sex and age, as it standardizes by them. This variable itself is a predictor of the outcome, achieving an auROC of 0.749 and auPR of 0.223, and acts as a baseline to compare and improve upon.

Risk Factors Analysis from the Prediction Model

Risk factors were investigated by analyzing which features attribute to the model's prediction. To this end, the recently introduced SHAP (SHapley Additive exPlanation) method (Lundberg and Lee 2017; Lundberg et al. 2018) was used. The SHAP interprets the output of a machine learning model. A feature's Shapley value represents the average change in the model's output by conditioning on that feature when introducing features one at a time over all feature orderings. Shapley values were calculated individually for every child's feature. A property of Shapley values is that they are additive, meaning that the Shapley values of a child's features add up to the predicted log-odds of obesity for that child. In this Example, this value was transformed for each feature and each child to obtain a relative risk score.

Feature attributions were thus analyzed at the individual level, by examining plots of the Shapley value as a function of the feature value for all individuals. This method allowed capturing non-linear and continuous relations between a feature's impact on the prediction and the feature's value. A vertical spread in such a plot implies interaction with other features in the model, which would not have been attainable using a linear model. Building a model with many correlated features (e.g., a child's weight measurement at adjacent time points) is bound to suffer from severe collinearity of the features, and consequently the feature attributions will be spread across these related features. To tackle this, the additive property of Shapley values was used. Adding up the Shapely values of related features provided an analysis on this group of features. This provided better estimates of relevant risk scores. Another use of the additive property allows adding features according to groups and analyzing the model globally by taking the mean over absolute Shapely values of all children in each group of features. This gives insight on the impact of a feature group.

Results

Acceleration of BMI in Early Childhood

BMI trajectories were first analyzed in early childhood in relation to obesity status at 13-14 years of age. A total of 382,132 children with 1,401,803 measurements were included in the analysis (FIGS. 4A and 4B). The mean change in BMI z-score of children who were not obese at 13 years of age remained close to 0 from 1 year of age, with an annual change of less than 0.1 z-scores. However, for obese children at 13 years of age, the BMI z-score incremented throughout infancy and early childhood with the largest annual increase in BMI percentile observed at 2-5 years of age. A model has therefore been developed in accordance with some embodiments of the present invention to identify children at high risk for obesity within the subsequent 3-4 years at 2 years of age, prior to this critical time period.

The transition of obesity status over the first 6 years of life for the 136,196 children that were included in our cohort was analyzed. Obesity status was defined for each child at two time-points: the last available routine checkup before 2 years of age and at 5-6 years of age (FIG. 4C). This analysis revealed that most obese children at 5-6 years of age had normal weight at infancy (51%) (FIG. 4D).

Prediction of Childhood Obesity at 5-6 Years of Age

In accordance with some embodiments of the present invention, a model was constructed for predicting the likelihood for children at 0-2 years of age to develop childhood obesity at 5 to 6 years of age. The discrimination performance of the model was evaluated using the area under the receiver operating (auROC) and precision-recall (auPR) curves (FIGS. 5A and 5C). As shown, the technique of the present embodiments outperforms the baseline model based on the child's last WFL percentile. Both temporal and geographical validation results are summarized in Table 2.2, below.

The model of the present embodiments outputs calibrated continuous risk probabilities. Applying a clinical decision thereafter (for example, a nutritional intervention) can vary between individuals and depend on the costs and benefits of the action, both clinically and economically. Decision curves (Vickers and Elkin 2006) offer a graphical tool to analyze clinical utility of adopting a new risk prediction model. The curves contain information that can guide clinicians to make decisions based on the risk thresholds, and based on the tradeoffs (costs and benefits) of their decision to treat. The costs and benefits can be translated into a function of the optimal threshold probability. In this Example, clinical utility was analyzed by constructing decision curves (FIG. 5D). As shown, the model of the present embodiments dominates over other strategies in net benefit over all threshold probabilities, with significant margins in the lower threshold probability regime. A summary of the effect of applying different decision thresholds on the model performance is presented in Table 2.2, below.

The discrimination results (auPR) of the model of the present embodiments were further analyzed on different subpopulations of children (FIGS. 6A-C). The effect of gender on the performance of the model was evaluated. Similar results for boys and girls were found. Children who had at least one diagnosis of a complex chronic condition were evaluated using a previously defined classification system (Feudtner et al. 2014). The discrimination of the model was similar in this group, demonstrating the robustness of the model of the present embodiments across a clinically heterogeneous pediatric population. Discrimination performance was also evaluated by obesity status as defined by the last available child percentile prior to 2 years of age. The model of the present embodiments had the highest auPR in children who were obese at infancy, followed by overweight and normal weight at infancy. The model of the present embodiments outperformed the baseline model in predicting future obesity in all infants, regardless of obesity status at baseline (FIG. 6B). An increase in the number of documented anthropometric measurements during routine checkups improved the discrimination performance of the model.

As earlier detection of childhood obesity may be more beneficial and allow earlier interventions, the ability to construct a prediction model for childhood obesity at the age 5-6 years of age was analyzed in the following time points: pre-birth, birth, 6 months, 1 year and 1.5 years of age. The effect of the child's age at prediction and the model discrimination performance is presented in FIG. 8A. As shown, the model performance improved when the prediction is done at an older age, which is closer to the target age of the predictor. Note that a prediction model constructed pre-birth has an auROC of 0.708 and auPR of 0.176, very similar to the performance of the baseline model based on the child's own weight and length measurements at 1 years of age which has an auROC of 0.709 and auPR of 0.166. The model of the present embodiments thus outperformed the baseline model in the entire age range.

Features Attribution

An analysis of feature attributions was performed using Shapley values. The results of the analysis are shown in FIGS. 7A-H. FIG. 7A presents a global analysis of the model's features attributions. The mean of absolute summation of Shapley values for different groups of features is presented for the entire cohort. Feature importance dependence plots of the Shapley value were also examined as a function of the feature value for all individuals. Most of the influential features were previous anthropometric measurements of the child, with the last measured WFL percentile being the most impactful feature (FIG. 7C). Anthropometric features of parents and siblings and North African Jewish descendancy also had a significant impact on the prediction (FIGS. 7A, 7D, 7E and 7H). Interestingly, maternal blood glucose on 50 g glucose tolerance tests (GTT) were also influential for the prediction of obesity at 5-6 years of age (FIG. 7F). Relative risk for obesity has increased monotonically across all the maternal glucose spectrum and increased above 1 in values above 100 mg/dL.

Analysis of the relative importance of different groups of features at different ages of applying the predictor revealed that the most influential features at birth are anthropometric measurements of the siblings, mother and father. Following these, the influence of the child's own anthropometrics measurements becomes more substantial and is roughly equal to the contribution of all other features in 1 years of age. Laboratory tests, drugs prescriptions and diagnoses have smaller relative influence, which decreases as the data on the child's anthropometrics accumulates (FIG. 8B).

Using information on pharmaceutical prescriptions, the effect of in utero and early life antibiotic exposure was also analyzed. 83,627 children (80%) had at least one antibiotic prescription in the first 2 years of life. The analysis revealed that antibiotic exposure in utero and in the first two years of life and age of first exposure to antibiotic had no effect on obesity risk at 5-6 years of age (FIG. 7G).

Prediction Model Based on a Smaller Number of Parameters

Based on the observation that infant routine checkups, family anthropometric measurements, and ethnicity contribute most to the predictive power of the model, a simple prediction model was established based on a set of self-assessed questions that parents can easily fill out at different time points up to 2 years of age in order to assess their child's risk of obesity. This model achieved an auROC of 0.798 and auPR of 0.296, compared to 0.749 and 0.223, respectively, for the baseline model.

Discussion

This Example demonstrates a diagnostic prediction model for pediatric obesity at 5-6 years of age based on a comprehensive nationwide EHR encompassing over 10 years of children and familial data. Overweight 5-year-olds are four times more likely to become obese later in life compared to normal-weight children, and weight in this age is considered to be a good indicator of the child's future metabolic health. The target age of prediction model presented in this Example is also supported by a recently published observation on children BMI trajectories (Geserick et al. 2018), which was also replicated in our cohort, showing 2 to 6 years of age as the maximal BMI acceleration time period. The model is therefore designed to identify children at risk prior to this critical time window, in which mature eating patterns become more developed as children reduce breast milk or formula consumption. In addition, the analysis of the transition in obesity status in the first 6 years of life revealed that most obese children had normal weight at infancy, underscoring the importance of building a tool that allows clinicians to identify high risk infants that are considered to have a normal weight at infancy but will develop obesity, as they will constitute the majority of obese children in the future.

The model presented in this Example achieved an auROC of 0.803 and auPR of 0.304. Further Analysis of prediction performance on subpopulations of the cohort demonstrated robustness in discrimination performance across the entire pediatric population, including children with complex chronic diseases. Unlike previous studies (Hammond et al. 2019), the results presented in this Example were similar for boys and girls. Additional models were further devised for predicting obesity prior to two years of age. High impact of family anthropometric measurements in determining future obesity risk of the child was demonstrated. This Example showed that a prediction model constructed pre-birth, which is mainly based on family anthropometric measurements has very similar performance of predicting at 1 years of age based on the child's last available weight and length measurements. A simple self-assessed questionnaire for childhood obesity prediction pre-birth achieved an auROC of 0.798 and auPR of 0.296.

The technique presented in this Example has several advantages over previous studies. The technique presented in this Example include full data on both the child, from pregnancy to 5-6 years of age, and his family, and is the first to be validated both temporally and geographically at different clinics on a national level, thus representing a wide target population. The technique presented in this Example is the first to assess clinical utility by constructing decision curves. To date, there are no clinical guidelines defining the risk threshold for obesity prediction. The definition of this threshold may be influenced by many factors, including the characteristics of the proposed intervention, the availability of resources for intervention and the prevalence of obesity in the target population, and will impact the sensitivity and specificity of the prediction model. The decision curve analysis presented in this Example may thus help in determining risk thresholds and the clinical usefulness of the model for different interventions.

The mechanisms involved in the development of obesity in children are complex and include genetic, environmental, and developmental factors. The large cohort of Israeli children represents a diverse and multi-ethnic population with genetic heterogeneity. Not surprisingly, many of the variables found to be important in the model were directly related to the child's previous anthropometric measurements. Familial anthropometric measurements, including paternal, maternal and sibling's BMI were also important, in line with previous studies showing associations between these variables and childhood obesity. Among familial data, sibling's BMI had the highest impact on the prediction model, most likely due to both genetic and environmental influences.

There is evidence that uterine environment may cause a permanent influence on fetus future health, and may lead to enhanced susceptibility to diseases later in life. This concept is defined as ‘gestational programming’ of the fetus, and is thought to be mediated by Epigenetic mechanisms (Desai et al. 2015; Desai and Hales 1997). The data on maternal pregnancy, including lab tests, diagnoses and medications was used to analyze associations of these features to obesity status of the offspring at 5-6 years of age. One of the most prominent features in pregnancy was maternal blood glucose values (FIG. 7F). An increase in maternal blood glucose levels during pregnancy, adjusted for other features incorporated in the model (such as maternal BMI), was associated with a higher risk for childhood obesity. This association, which was apparent even in glucose values which are considered in the normal range, demonstrates that exposure to higher glucose levels in utero throughout the entire maternal glucose spectrum is significantly associated with childhood glucose and insulin resistance of the offspring and is independently associated with childhood adiposity. Ethnicity as a risk factor has previously been studied in the UK and USA populations, in which a higher prevalence of obesity was found among children of African descent (Brophy et al. 2009). The analysis presented in This Example concentrated on the Israeli population, and revealed North African Jewish descendancy as a strong contributor for predicting obesity.

The role of the gut microbiota in obesity has been vastly studied in recent years (Castaner et al. 2018). Microbiome composition undergoes many changes during the first years of life (Stewart et al. 2018). Antibiotics, which are frequently prescribed in the pediatric population (Chai et al. 2012), can significantly alter the microbiome composition (Robinson and Young 2010). Therefore, several recent studies assessed the relationship between antibiotic usage in early life and childhood obesity. These resulted in conflicting findings (Shao et al. 2017). The large sample size and the data on antibiotic prescriptions in pregnancy and infancy used in this Example allowed to explore this association. The analysis presented in this Example revealed that while the vast majority (80%) of the cohort received antibiotics at least once by the age of 2 years of age, antibiotic exposure in utero and in the first two years of life, and age of first exposure to antibiotic, had no observed impact on the obesity risk at 5-6 years of age.

The data used in This Example is from a retrospective observational EHR. These may suffer from potential biases and are affected by a variety of healthcare processes. Sampling bias was minimized by choosing children based on the schedule of routine measurements of weight and height, which includes both measurements at 0-2 years of age and a measurement at 5-6 years of age.

It is noted that while the prediction model presented in this Example is based on data of Israeli children, the validation process, which included both a temporal and a geographical validation, the well-known universal risk factors for childhood obesity that were found in the analysis of the model, and the striking similarity of the analysis on BMI trajectories to an independent, recently published German cohort (Geserick et al. 2018), indicates that the results may be generalized to other populations as well.

TABLE 2.2
Prediction Results

Temporal test set

Geographical test set

Model	auPR	auROC	auPR	auROC
Baseline	0.223	0.749	0.177	0.736
	(0.209-0.235)	(0.739-0.758)	(0.162-0.201)	(0.712-0.755)
Full	0.304	0.803	0.251	0.789
Model	(0.286-0.321)	(0.796-0.812)	(0.230-0.280)	(0.771-0.805)
Abbreviations: auPR/auROC—Area under the PR/ROC curve, PR—Precision-Recall, ROC—Receiver-Operator-Characteristic

TABLE 2.3
Effects of varying decision threshold on model performance

Predicted probability threshold

	2%	5%	10%	20%	30%	40%	Baseline

Sensitivity	0.962	0.794	0.585	0.364	0.236	0.142	0.281
Specificity	0.257	0.651	0.840	0.946	0.977	0.991	0.949
PPV	0.089	0.146	0.215	0.334	0.435	0.533	0.291
NPV	0.989	0.977	0.964	0.952	0.945	0.939	0.946
Net Benefit	0.053	0.038	0.024	0.013	0.007	0.004
Abbreviations: NPV—Negative predictive value, PPV—positive predictive value

TABLE 2.4
Prediction of obesity at 5-6 years of age prior to 2 years of age

Age of applying

Temporal test set

Geographical test set

prediction	auPR	auROC	auPR	auROC

Pre-birth	Full	0.176	0.708	0.134	0.680
	Model	(0.168-0.188)	(0.689-0.723)	(0.125-0.153)	(0.660-0.704)
Birth	Full	0.177	0.711	0.134	0.684
	Model	(0.169-0.189)	(0.701-0.726)	(0.124-0.153)	(0.666-0.708)
6 months	Baseline	0.133	0.671	0.099	0.641
		(0.126-0.144)	(0.666-0.681)	(0.085-0.117)	(0.620-0.669)
	Full	0.230	0.759	0.174	0.728
	Model	(0.216-0.249)	(0.751-0.769)	(0.153-0.200)	(0.713-0.747)
12 months	Baseline	0.166	0.709	0.130	0.684
		(0.159-0.178)	(0.700-0.716)	(0.117-0.147)	(0.667-0.703)
	Full	0.249	0.777	0.204	0.755
	Model	(0.233-0.267)	(0.769-0.787)	(0.187-0.229)	(0.739-0.775)
18 months	Baseline	0.190	0.732	0.162	0.716
		(0.179-0.201)	(0.726-0.742)	(0.147-0.184)	(0.693-0.740)
	Full	0.278	0.791	0.230	0.775
	Model	(0.262-0.297)	(0.783-0.800)	(0.215-0.255)	(0.759-0.792)
2 years	Baseline	0.223	0.749	0.177	0.736
		(0.209-0.235)	(0.739-0.758)	(0.162-0.201)	(0.712-0.755)
	Full	0.304	0.803	0.251	0.789
	Model	(0.286-0.321)	(0.796-0.812)	(0.230-0.280)	(0.771-0.805)
Abbreviations: auPR/auROC—Area under the PR/ROC curve, PR—Precision-Recall, ROC—Receiver-Operator-Characteristic

Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is/are hereby incorporated herein by reference in its/their entirety.

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